Zihaoluoh / Awesome-Incremental-Learning

Awesome Incremental Learning

Awesome Incremental Learning / Lifelong learning

Survey

• Class-Incremental Learning: A Survey (TPAMI 2024) [paper][code]
• Continual Learning with Pre-Trained Models: A Survey (IJCAI 2024) [paper][code]
• Continual Learning of Large Language Models: A Comprehensive Survey (arXiv 2024) [paper][code]
• A Comprehensive Survey of Continual Learning: Theory, Method and Application (TPAMI 2024) [paper]
• A Comprehensive Empirical Evaluation on Online Continual Learning (ICCV Workshop 2023) [paper][code]
• A Survey on Few-Shot Class-Incremental Learning (Neural Networks 2024) [paper]
• A wholistic view of continual learning with deep neural networks: Forgotten lessons and the bridge to active and open world learning (Neural Networks 2023) [paper]
• An Introduction to Lifelong Supervised Learning (arXiv 2022) [paper]
• A Survey on Incremental Update for Neural Recommender Systems (arXiv 2023) [paper]
• Continual Learning of Natural Language Processing Tasks: A Survey (arXiv 2022) [paper]
• Continual Learning for Real-World Autonomous Systems: Algorithms, Challenges and Frameworks (arXiv 2022) [paper]
• Recent Advances of Continual Learning in Computer Vision: An Overview (arXiv 2021) [paper]
• Replay in Deep Learning: Current Approaches and Missing Biological Elements (Neural Computation 2021) [paper]
• Online Continual Learning in Image Classification: An Empirical Survey (Neurocomputing 2021) [paper] [code]
• Continual Lifelong Learning in Natural Language Processing: A Survey (COLING 2020) [paper]
• Class-incremental learning: survey and performance evaluation (TPAMI 2022) [paper] [code]
• A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (Neural Networks) [paper] [code]
• A continual learning survey: Defying forgetting in classification tasks (TPAMI 2021) [paper] [arxiv]
• Continual Lifelong Learning with Neural Networks: A Review (Neural Networks) [paper]
• Three scenarios for continual learning (Nature Machine Intelligence 2022) [paper][code]

Papers

2024

• Harnessing Neural Unit Dynamics for Effective and Scalable Class-Incremental Learning (ICML24)[paper]

• Multi-layer Rehearsal Feature Augmentation for Class-Incremental Learning (ICML24)[paper][code]

• Regularizing with Pseudo-Negatives for Continual Self-Supervised Learning (ICML24)[paper]

• Learning to Continually Learn with the Bayesian Principle (ICML24)[paper][code]

• Rethinking Momentum Knowledge Distillation in Online Continual Learning (ICML24)[paper][code]

• Layerwise Proximal Replay: A Proximal Point Method for Online Continual Learning (ICML24)[paper]

• Bayesian Adaptation of Network Depth and Width for Continual Learning (ICML24)[paper]

• STELLA: Continual Audio-Video Pre-training with SpatioTemporal Localized Alignment (ICML24)[paper][code]

• On the Diminishing Returns of Width for Continual Learning (ICML24)[paper][code]

• Compositional Few-Shot Class-Incremental Learning (ICML24)[paper][code]

• Rapid Learning without Catastrophic Forgetting in the Morris Water Maze (ICML24)[paper][code]

• Understanding Forgetting in Continual Learning with Linear Regression (ICML24)[paper]

• Mitigating Catastrophic Forgetting in Online Continual Learning by Modeling Previous Task Interrelations via Pareto Optimization (ICML24)[paper]

• Task-aware Orthogonal Sparse Network for Exploring Shared Knowledge in Continual Learning (ICML24)[paper]

• Provable Contrastive Continual Learning (ICML24)[paper]

• Gradual Divergence for Seamless Adaptation: A Novel Domain Incremental Learning Method (ICML24)[paper][code]

• An Effective Dynamic Gradient Calibration Method for Continual Learning (ICML24)[paper]

• Federated Continual Learning via Prompt-based Dual Knowledge Transfer (ICML24)[paper][code]

• COPAL: Continual Pruning in Large Language Generative Models (ICML24)[paper]

• One Size Fits All for Semantic Shifts: Adaptive Prompt Tuning for Continual Learning (ICML24)[paper]

• Hierarchical Augmentation and Distillation for Class Incremental Audio-Visual Video Recognition (TPAMI2024)[paper]

• Continual Segmentation with Disentangled Objectness Learning and Class Recognition (CVPR2024)[paper][code]

• Interactive Continual Learning: Fast and Slow Thinking (CVPR2024)[paper][code]

• InfLoRA: Interference-Free Low-Rank Adaptation for Continual Learning (CVPR2024)[paper][code]

• Semantically-Shifted Incremental Adapter-Tuning is A Continual ViTransformer (CVPR2024)[paper][code]

• Traceable Federated Continual Learning (CVPR2024)[paper][code]

• Defense without Forgetting: Continual Adversarial Defense with Anisotropic & Isotropic Pseudo Replay (CVPR2024)[paper]

• Learning Continual Compatible Representation for Re-indexing Free Lifelong Person Re-identification (CVPR2024)[paper][code]

• Towards Backward-Compatible Continual Learning of Image Compression (CVPR2024)[paper][code]

• Class Incremental Learning with Multi-Teacher Distillation (CVPR2024)[paper][code]

• Towards Efficient Replay in Federated Incremental Learning (CVPR2024)[paper]

• Dual-consistency Model Inversion for Non-exemplar Class Incremental Learning (CVPR2024)[paper]

• Dual-Enhanced Coreset Selection with Class-wise Collaboration for Online Blurry Class Incremental Learning (CVPR2024)[paper]

• Coherent Temporal Synthesis for Incremental Action Segmentation (CVPR2024)[paper]

• Text-Enhanced Data-free Approach for Federated Class-Incremental Learning (CVPR2024)[paper][code]

• NICE: Neurogenesis Inspired Contextual Encoding for Replay-free Class Incremental Learning (CVPR2024)[paper][code]

• Long-Tail Class Incremental Learning via Independent Sub-prototype Construction (CVPR2024)[paper]

• FCS: Feature Calibration and Separation for Non-Exemplar Class Incremental Learning (CVPR2024)[paper][code]

• Incremental Nuclei Segmentation from Histopathological Images via Future-class Awareness and Compatibility-inspired Distillation (CVPR2024)[paper][code]

• Gradient Reweighting: Towards Imbalanced Class-Incremental Learning (CVPR2024)[paper][code]

• OrCo: Towards Better Generalization via Orthogonality and Contrast for Few-Shot Class-Incremental Learning (CVPR2024)[paper][code]

• SDDGR: Stable Diffusion-based Deep Generative Replay for Class Incremental Object Detection (CVPR2024)[paper]

• Generative Multi-modal Models are Good Class Incremental Learners (CVPR2024)[paper][code]

• Task-Adaptive Saliency Guidance for Exemplar-free Class Incremental Learning (CVPR2024)[paper][code]

• DYSON: Dynamic Feature Space Self-Organization for Online Task-Free Class Incremental Learning (CVPR2024)[paper][code]

• Enhancing Visual Continual Learning with Language-Guided Supervision (CVPR2024)[paper]

• Boosting Continual Learning of Vision-Language Models via Mixture-of-Experts Adapters (CVPR2024)[paper][code]

• Adaptive VIO: Deep Visual-Inertial Odometry with Online Continual Learning (CVPR2024)[paper]

• Continual Self-supervised Learning: Towards Universal Multi-modal Medical Data Representation Learning (CVPR2024)[paper][code]

• ECLIPSE: Efficient Continual Learning in Panoptic Segmentation with Visual Prompt Tuning (CVPR2024)[paper][code]

• Online Task-Free Continual Generative and Discriminative Learning via Dynamic Cluster Memory (CVPR2024)[paper][code]

• Learning from One Continuous Video Stream (CVPR2024)[paper]

• Improving Plasticity in Online Continual Learning via Collaborative Learning (CVPR2024)[paper][code]

• Learning Equi-angular Representations for Online Continual Learning (CVPR2024)[paper][code]

• BrainWash: A Poisoning Attack to Forget in Continual Learning (CVPR2024)[paper]

• Consistent Prompting for Rehearsal-Free Continual Learning (CVPR2024)[paper][code]

• Resurrecting Old Classes with New Data for Exemplar-Free Continual Learning (CVPR2024)[paper][code]

• Convolutional Prompting meets Language Models for Continual Learning (CVPR2024)[paper][code]

• Expandable Subspace Ensemble for Pre-Trained Model-Based Class-Incremental Learning (CVPR2024)[paper][code]

• Pre-trained Vision and Language Transformers Are Few-Shot Incremental Learners (CVPR2024)[paper][code]

• Orchestrate Latent Expertise: Advancing Online Continual Learning with Multi-Level Supervision and Reverse Self-Distillation (CVPR2024)[paper][code]

• Elastic Feature Consolidation For Cold Start Exemplar-Free Incremental Learning (ICLR2024)[paper][code]

• Function-space Parameterization of Neural Networks for Sequential Learning (ICLR2024)[paper]

• Progressive Fourier Neural Representation for Sequential Video Compilation (ICLR2024)[paper]

• Kalman Filter Online Classification from non-Stationary Data (ICLR2024)[paper]

• Continual Momentum Filtering on Parameter Space for Online Test-time Adaptation (ICLR2024)[paper]

• TAIL: Task-specific Adapters for Imitation Learning with Large Pretrained Models (ICLR2024)[paper]

• Class Incremental Learning via Likelihood Ratio Based Task Prediction (ICLR2024)[paper][code]

• The Joint Effect of Task Similarity and Overparameterization on Catastrophic Forgetting - An Analytical Model (ICLR2024)[paper]

• Prediction Error-based Classification for Class-Incremental Learning (ICLR2024)[paper][code]

• Adapting Large Language Models via Reading Comprehension (ICLR2024)[paper][code]

• Accurate Forgetting for Heterogeneous Federated Continual Learning (ICLR2024)[paper]

• Fixed Non-negative Orthogonal Classifier: Inducing Zero-mean Neural Collapse with Feature Dimension Separation (ICLR2024)[paper]

• A Probabilistic Framework for Modular Continual Learning (ICLR2024)[paper]

• A Unified and General Framework for Continual Learning (ICLR2024)[paper]

• Continual Learning on a Diet: Learning from Sparsely Labeled Streams Under Constrained Computation (ICLR2024)[paper]

• CPPO: Continual Learning for Reinforcement Learning with Human Feedback (ICLR2024)[paper]

• Online Continual Learning for Interactive Instruction Following Agents (ICLR2024)[paper][code]

• Scalable Language Model with Generalized Continual Learning (ICLR2024)[paper]

• ViDA: Homeostatic Visual Domain Adapter for Continual Test Time Adaptation (ICLR2024)[paper]

• Hebbian Learning based Orthogonal Projection for Continual Learning of Spiking Neural Networks (ICLR2024)[paper][code]

• TiC-CLIP: Continual Training of CLIP Models (ICLR2024)[paper]

• Continual Learning in the Presence of Spurious Correlations: Analyses and a Simple Baseline (ICLR2024)[paper]

• Addressing Catastrophic Forgetting and Loss of Plasticity in Neural Networks (ICLR2024)[paper]

• Locality Sensitive Sparse Encoding for Learning World Models Online (ICLR2024)[paper]

• Dissecting learning and forgetting in language model finetuning (ICLR2024)[paper]

• Prompt Gradient Projection for Continual Learning (ICLR2024)[paper][code]

• Latent Trajectory Learning for Limited Timestamps under Distribution Shift over Time (ICLR2024)[paper]

• Divide and not forget: Ensemble of selectively trained experts in Continual Learning (ICLR2024)[paper][code]

• eTag: Class-Incremental Learning via Embedding Distillation and Task-Oriented Generation (AAAI2024) [paper][code]

• Evolving Parameterized Prompt Memory for Continual Learning (AAAI2024)[paper][code]

• Towards Continual Learning Desiderata via HSIC-Bottleneck Orthogonalization and Equiangular Embedding (AAAI2024)[paper]

• Fine-Grained Knowledge Selection and Restoration for Non-Exemplar Class Incremental Learning (AAAI2024)[paper]

• Class-Incremental Learning: Cross-Class Feature Augmentation for Class Incremental Learning (AAAI2024)[paper]

• MIND: Multi-Task Incremental Network Distillation (AAAI2024)[paper][code]

• Adapt Your Teacher: Improving Knowledge Distillation for Exemplar-free Continual Learning (WACV2024)[paper][code]

• Plasticity-Optimized Complementary Networks for Unsupervised Continual (WACV2024)[paper]

• Online Class-Incremental Learning For Real-World Food Image Classification (WACV2024)[paper]

2023

• SIESTA: Efficient Online Continual Learning with Sleep (TMLR 2023)[paper]
• Sub-network Discovery and Soft-masking for Continual Learning of Mixed Tasks (EMNLP 2023)[paper]
• Incorporating neuro-inspired adaptability for continual learning in artificial intelligence (Nature Machine Intelligence 2023) [paper]
• Enhancing Knowledge Transfer for Task Incremental Learning with Data-free Subnetwork (NeurIPS 2023) [paper] [Code]
• Loss Decoupling for Task-Agnostic Continual Learning (NeurIPS 2023) [paper]
• Bilevel Coreset Selection in Continual Learning: A New Formulation and Algorithm (NeurIPS 2023)[paper]
• Fairness Continual Learning Approach to Semantic Scene Understanding in Open-World Environments (NeurIPS 2023)[paper]
• An Efficient Dataset Condensation Plugin and Its Application to Continual Learning (NeurIPS 2023)[paper]
• Overcoming Recency Bias of Normalization Statistics in Continual Learning: Balance and Adaptation (NeurIPS 2023)[paper]
• Prediction and Control in Continual Reinforcement Learning (NeurIPS 2023)[paper]
• On the Stability-Plasticity Dilemma in Continual Meta-Learning: Theory and Algorithm (NeurIPS 2023)[paper]
• Saving 100x Storage: Prototype Replay for Reconstructing Training Sample Distribution in Class-Incremental Semantic Segmentation (NeurIPS 2023)[paper]
• A Data-Free Approach to Mitigate Catastrophic Forgetting in Federated Class Incremental Learning for Vision Tasks (NeurIPS 2023)[paper]
• Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration (NeurIPS 2023)[paper]
• A Unified Approach to Domain Incremental Learning with Memory: Theory and Algorithm (NeurIPS 2023)[paper][code]
• Minimax Forward and Backward Learning of Evolving Tasks with Performance Guarantees (NeurIPS 2023)[paper][code]
• Recasting Continual Learning as Sequence Modeling (NeurIPS 2023)[paper]
• Augmented Memory Replay-based Continual Learning Approaches for Network Intrusion Detection (NeurIPS 2023)[paper]
• Does Continual Learning Meet Compositionality? New Benchmarks and An Evaluation Framework (NeurIPS 2023)[paper]
• CL-NeRF: Continual Learning of Neural Radiance Fields for Evolving Scene Representation (NeurIPS 2023)[paper]
• TriRE: A Multi-Mechanism Learning Paradigm for Continual Knowledge Retention and Promotion (NeurIPS 2023)[paper]
• Selective Amnesia: A Continual Learning Approach to Forgetting in Deep Generative Models (NeurIPS 2023)[paper]
• A Definition of Continual Reinforcement Learning (NeurIPS 2023)[paper]
• RanPAC: Random Projections and Pre-trained Models for Continual Learning (NeurIPS 2023)[paper]
• Hierarchical Decomposition of Prompt-Based Continual Learning: Rethinking Obscured Sub-optimality (NeurIPS 2023)[paper]
• FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning (NeurIPS 2023)[paper]
• The Ideal Continual Learner: An Agent That Never Forgets (ICML2023) [paper]
• Continual Learners are Incremental Model Generalizers (ICML2023)[paper]
• Learnability and Algorithm for Continual Learning (ICML2023)[paper][code]
• Parameter-Level Soft-Masking for Continual Learning (ICML2023)[paper]
• Continual Learning in Linear Classification on Separable Data (ICML2023)[paper]
• DualHSIC: HSIC-Bottleneck and Alignment for Continual Learning (ICML2023)[paper]
• BiRT: Bio-inspired Replay in Vision Transformers for Continual Learning (ICML2023)[paper]
• DDGR: Continual Learning with Deep Diffusion-based Generative Replay (ICML2023)[paper]
• Neuro-Symbolic Continual Learning: Knowledge, Reasoning Shortcuts and Concept Rehearsal (ICML2023)[paper]
• Theory on Forgetting and Generalization of Continual Learning (ICML2023)[paper]
• Poisoning Generative Replay in Continual Learning to Promote Forgetting (ICML2023)[paper]
• Continual Vision-Language Representation Learning with Off-Diagonal Information (ICML2023)[paper]
• Prototype-Sample Relation Distillation: Towards Replay-Free Continual Learning (ICML2023)[paper]
• Does Continual Learning Equally Forget All Parameters? (ICML2023)[paper]
• Growing a Brain with Sparsity-Inducing Generation for Continual Learning (ICCV 2023)[paper][code]
• Self-regulating Prompts: Foundational Model Adaptation without Forgetting (ICCV 2023)[paper][code]
• Prototype Reminiscence and Augmented Asymmetric Knowledge Aggregation for Non-Exemplar Class-Incremental Learning (ICCV 2023)[paper][code]
• Tangent Model Composition for Ensembling and Continual Fine-tuning (ICCV 2023)[paper][code]
• CBA: Improving Online Continual Learning via Continual Bias Adaptor (ICCV 2023)[paper]
• CTP: Towards Vision-Language Continual Pretraining via Compatible Momentum Contrast and Topology Preservation (ICCV 2023)[paper][code]
• NAPA-VQ: Neighborhood Aware Prototype Augmentation with Vector Quantization for Continual Learning (ICCV 2023)[paper][code]
• Online Continual Learning on Hierarchical Label Expansion (ICCV 2023)[paper]
• Class-Incremental Grouping Network for Continual Audio-Visual Learning (ICCV 2023)[paper][code]
• Rapid Adaptation in Online Continual Learning: Are We Evaluating It Right? (ICCV 2023)[paper][code]
• When Prompt-based Incremental Learning Does Not Meet Strong Pretraining (ICCV 2023)[paper]
• Online Class Incremental Learning on Stochastic Blurry Task Boundary via Mask and Visual Prompt Tuning (ICCV 2023)[paper][code]
• Dynamic Residual Classifier for Class Incremental Learning (ICCV 2023)[paper]
• First Session Adaptation: A Strong Replay-Free Baseline for Class-Incremental Learning (ICCV 2023)[paper]
• Masked Autoencoders are Efficient Class Incremental Learners (ICCV 2023)[paper]
• Introducing Language Guidance in Prompt-based Continual Learning (ICCV 2023)[paper]
• CLNeRF: Continual Learning Meets NeRFs (ICCV 2023)[paper]
• Preventing Zero-Shot Transfer Degradation in Continual Learning of Vision-Language Models (ICCV 2023)[paper][code]
• LFS-GAN: Lifelong Few-Shot Image Generation (ICCV 2023)[paper]
• TARGET: Federated Class-Continual Learning via Exemplar-Free Distillation (ICCV 2023)[paper]
• Learning to Learn: How to Continuously Teach Humans and Machines (ICCV 2023)[paper]
• Audio-Visual Class-Incremental Learning (ICCV 2023)[paper][code]
• MetaGCD: Learning to Continually Learn in Generalized Category Discovery (ICCV 2023)[paper]
• Exemplar-Free Continual Transformer with Convolutions (ICCV 2023)[paper][code]
• A Unified Continual Learning Framework with General Parameter-Efficient Tuning (ICCV 2023)[paper]
• Incremental Generalized Category Discovery (ICCV 2023)[paper]
• Heterogeneous Forgetting Compensation for Class-Incremental Learning (ICCV 2023)[paper][code]
• Augmented Box Replay: Overcoming Foreground Shift for Incremental Object Detection (ICCV 2023)[paper][code]
• MRN: Multiplexed Routing Network for Incremental Multilingual Text Recognition (ICCV 2023)[paper][code]
• CLR: Channel-wise Lightweight Reprogramming for Continual Learning (ICCV 2023)[paper][code]
• ICICLE: Interpretable Class Incremental Continual Learning (ICCV 2023)[paper]
• Proxy Anchor-based Unsupervised Learning for Continuous Generalized Category Discovery (ICCV 2023)[paper]
• SLCA: Slow Learner with Classifier Alignment for Continual Learning on a Pre-trained Model (ICCV 2023)[paper][code]
• Online Prototype Learning for Online Continual Learning (ICCV 2023)[paper][code]
• Analyzing and Reducing the Performance Gap in Cross-Lingual Transfer with Fine-tuning Slow and Fast (ACL2023)[paper]
• Class-Incremental Learning based on Label Generation (ACL2023)[paper]
• Computationally Budgeted Continual Learning: What Does Matter? (CVPR2023)[paper][code]
• Real-Time Evaluation in Online Continual Learning: A New Hope (CVPR2023)[paper]
• Dealing With Cross-Task Class Discrimination in Online Continual Learning (CVPR2023)[paper][code]
• Decoupling Learning and Remembering: A Bilevel Memory Framework With Knowledge Projection for Task-Incremental Learning (CVPR2023)[paper][code]
• GKEAL: Gaussian Kernel Embedded Analytic Learning for Few-shot Class Incremental Task (CVPR2023)[paper]
• EcoTTA: Memory-Efficient Continual Test-time Adaptation via Self-distilled Regularization (CVPR2023)[paper]
• Endpoints Weight Fusion for Class Incremental Semantic Segmentation (CVPR2023)[paper]
• On the Stability-Plasticity Dilemma of Class-Incremental Learning (CVPR2023)[paper]
• Regularizing Second-Order Influences for Continual Learning (CVPR2023)[paper][code]
• Rebalancing Batch Normalization for Exemplar-based Class-Incremental Learning (CVPR2023)[paper]
• Task Difficulty Aware Parameter Allocation & Regularization for Lifelong Learning (CVPR2023)[paper]
• A Probabilistic Framework for Lifelong Test-Time Adaptation (CVPR2023)[paper][code]
• Continual Semantic Segmentation with Automatic Memory Sample Selection (CVPR2023)[paper]
• Exploring Data Geometry for Continual Learning (CVPR2023)[paper]
• PCR: Proxy-based Contrastive Replay for Online Class-Incremental Continual Learning (CVPR2023)[paper][code]
• Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning (CVPR2023)[paper][code]
• Foundation Model Drives Weakly Incremental Learning for Semantic Segmentation (CVPR2023)[paper]
• Continual Detection Transformer for Incremental Object Detection (CVPR2023)[paper][code]
• PIVOT: Prompting for Video Continual Learning (CVPR2023)[paper]
• CODA-Prompt: COntinual Decomposed Attention-based Prompting for Rehearsal-Free Continual Learning (CVPR2023)[paper][code]
• Principles of Forgetting in Domain-Incremental Semantic Segmentation in Adverse Weather Conditions (CVPR2023)[paper]
• Class-Incremental Exemplar Compression for Class-Incremental Learning (CVPR2023)[paper][code]
• Dense Network Expansion for Class Incremental Learning (CVPR2023)[paper]
• Online Bias Correction for Task-Free Continual Learning (ICLR2023)[paper]
• Sparse Distributed Memory is a Continual Learner (ICLR2023)[paper]
• Continual Learning of Language Models (ICLR2023)[paper]
• Progressive Prompts: Continual Learning for Language Models without Forgetting (ICLR2023)[paper]
• Is Forgetting Less a Good Inductive Bias for Forward Transfer? (ICLR2023)[paper]
• Online Boundary-Free Continual Learning by Scheduled Data Prior (ICLR2023)[paper]
• Incremental Learning of Structured Memory via Closed-Loop Transcription (ICLR2023)[paper]
• Better Generative Replay for Continual Federated Learning (ICLR2023)[paper]
• 3EF: Class-Incremental Learning via Efficient Energy-Based Expansion and Fusion (ICLR2023)[paper]
• Progressive Voronoi Diagram Subdivision Enables Accurate Data-free Class-Incremental Learning (ICLR2023)[paper]
• Learning without Prejudices: Continual Unbiased Learning via Benign and Malignant Forgetting (ICLR2023)[paper]
• Building a Subspace of Policies for Scalable Continual Learning (ICLR2023)[paper]
• A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning (ICLR2023)[paper]
• Continual evaluation for lifelong learning: Identifying the stability gap (ICLR2023)[paper]
• Continual Unsupervised Disentangling of Self-Organizing Representations (ICLR2023)[paper]
• Warping the Space: Weight Space Rotation for Class-Incremental Few-Shot Learning (ICLR2023)[paper]
• Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning (ICLR2023)[paper]
• On the Soft-Subnetwork for Few-Shot Class Incremental Learning (ICLR2023)[paper]
• Task-Aware Information Routing from Common Representation Space in Lifelong Learning (ICLR2023)[paper]
• Error Sensitivity Modulation based Experience Replay: Mitigating Abrupt Representation Drift in Continual Learning (ICLR2023)[paper]
• Neural Weight Search for Scalable Task Incremental Learning (WACV2023)[paper]
• Attribution-aware Weight Transfer: A Warm-Start Initialization for Class-Incremental Semantic Segmentation (WACV2023)[paper]
• FeTrIL: Feature Translation for Exemplar-Free Class-Incremental Learning (WACV2023)[paper]
• Do Pre-trained Models Benefit Equally in Continual Learning? (WACV2023)[paper] [code]
• Sparse Coding in a Dual Memory System for Lifelong Learning (AAAI2023)[paper] [code]

2022

• Online Continual Learning through Mutual Information Maximization (ICML2022)[paper]

• Prototype-guided continual adaptation for class-incremental unsupervised domain adaptation (ECCV2022)[paper] [code]

• Balanced softmax cross-entropy for incremental learning with and without memory (CVIU)[paper]

• Incremental Prompting: Episodic Memory Prompt for Lifelong Event Detection (COLING2022) [paper] [code]

• Improving Task-free Continual Learning by Distributionally Robust Memory Evolution (ICML2022)[paper]

• Forget-free Continual Learning with Winning Subnetworks (ICML2022)[paper]

• NISPA: Neuro-Inspired Stability-Plasticity Adaptation for Continual Learning in Sparse Networks (ICML2022)[paper]

• Continual Learning via Sequential Function-Space Variational Inference (ICML2022)[paper]

• A Theoretical Study on Solving Continual Learning (NeurIPS2022) [paper] [code]

• ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection (NeurIPS2022) [paper]

• Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer (NeurIPS2022) [paper]

• Memory Efficient Continual Learning with Transformers (NeurIPS2022) [paper]

• Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation (NeurIPS2022) [paper] [code]

• Disentangling Transfer in Continual Reinforcement Learning (NeurIPS2022) [paper]

• Task-Free Continual Learning via Online Discrepancy Distance Learning (NeurIPS2022) [paper]

• A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal (NeurIPS2022) [paper]

• S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning (NeurIPS2022) [paper]

• Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting (NeurIPS2022) [paper]

• Few-Shot Continual Active Learning by a Robot (NeurIPS2022) [paper]

• Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions(NeurIPS2022) [paper]

• SparCL: Sparse Continual Learning on the Edge(NeurIPS2022) [paper]

• CLiMB: A Continual Learning Benchmark for Vision-and-Language Tasks (NeurIPS2022) [paper] [code]

• Continual Learning In Environments With Polynomial Mixing Times (NeurIPS2022) [paper] [code]

• Exploring Example Influence in Continual Learning (NeurIPS2022) [paper] [code]

• ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation (NeurIPS2022) [paper]

• On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning (NeurIPS2022) [paper] [code]

• On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting (NeurIPS2022)[paper]

• CGLB: Benchmark Tasks for Continual Graph Learning (NeurIPS2022)[paper] [code]

• How Well Do Unsupervised Learning Algorithms Model Human Real-time and Life-long Learning? (NeurIPS2022)[paper]

• CoSCL: Cooperation of Small Continual Learners is Stronger than a Big One (ECCV2022)[paper] [code]

• Generative Negative Text Replay for Continual Vision-Language Pretraining (ECCV2022) [paper]

• DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning (ECCV2022) [paper] [code]

• The Challenges of Continuous Self-Supervised Learning (ECCV2022)[paper]

• Helpful or Harmful: Inter-Task Association in Continual Learning (ECCV2022)[paper]

• incDFM: Incremental Deep Feature Modeling for Continual Novelty Detection (ECCV2022)[paper]

• S3C: Self-Supervised Stochastic Classifiers for Few-Shot Class-Incremental Learning (ECCV2022)[paper]

• Online Task-free Continual Learning with Dynamic Sparse Distributed Memory (ECCV2022)[paper][code]

• Balancing between Forgetting and Acquisition in Incremental Subpopulation Learning (ECCV2022)[paper]

• Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer (ECCV2022) [paper] [code]

• FOSTER: Feature Boosting and Compression for Class-Incremental Learning (ECCV2022) [paper] [code]

• Meta-Learning with Less Forgetting on Large-Scale Non-Stationary Task Distributions (ECCV2022) [paper]

• R-DFCIL: Relation-Guided Representation Learning for Data-Free Class Incremental Learning (ECCV2022) [paper] [code]

• DLCFT: Deep Linear Continual Fine-Tuning for General Incremental Learning (ECCV2022) [paper]

• Learning with Recoverable Forgetting (ECCV2022) [paper]

• Prototype-Guided Continual Adaptation for Class-Incremental Unsupervised Domain Adaptation (ECCV2022) [paper] [code]

• Balancing Stability and Plasticity through Advanced Null Space in Continual Learning (ECCV2022) [paper]

• Long-Tailed Class Incremental Learning (ECCV2022) [paper]

• Anti-Retroactive Interference for Lifelong Learning (ECCV2022) [paper]

• Novel Class Discovery without Forgetting (ECCV2022) [paper]

• Class-incremental Novel Class Discovery (ECCV2022) [paper]

• Few-Shot Class Incremental Learning From an Open-Set Perspective(ECCV2022)[paper]

• Incremental Task Learning with Incremental Rank Updates(ECCV2022)[paper]

• Few-Shot Class-Incremental Learning via Entropy-Regularized Data-Free Replay(ECCV2022)[paper]

• Online Continual Learning with Contrastive Vision Transformer (ECCV2022)[paper]

• Transfer without Forgetting (ECCV2022) [paper][code]

• Continual Training of Language Models for Few-Shot Learning (EMNLP2022) [paper] [code]

• Uncertainty-aware Contrastive Distillation for Incremental Semantic Segmentation (TPAMI2022) [paper]

• MgSvF: Multi-Grained Slow vs. Fast Framework for Few-Shot Class-Incremental Learning (TPAMI2022) [paper]

• Class-Incremental Continual Learning into the eXtended DER-verse (TPAMI2022) [paper] [code]

• Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks (TPAMI2022) [paper] [code]

• Continual Semi-Supervised Learning through Contrastive Interpolation Consistency (PRL2022) [paper][code]

• GCR: Gradient Coreset Based Replay Buffer Selection for Continual Learning (CVPR2022) [paper]

• Learning Bayesian Sparse Networks With Full Experience Replay for Continual Learning (CVPR2022) [paper]

• Continual Learning With Lifelong Vision Transformer (CVPR2022) [paper]

• Towards Better Plasticity-Stability Trade-Off in Incremental Learning: A Simple Linear Connector (CVPR2022) [paper]

• Doodle It Yourself: Class Incremental Learning by Drawing a Few Sketches (CVPR2022) [paper]

• Continual Learning for Visual Search with Backward Consistent Feature Embedding (CVPR2022) [paper]

• Online Continual Learning on a Contaminated Data Stream with Blurry Task Boundaries (CVPR2022) [paper]

• Not Just Selection, but Exploration: Online Class-Incremental Continual Learning via Dual View Consistency (CVPR2022) [paper]

• Bring Evanescent Representations to Life in Lifelong Class Incremental Learning (CVPR2022) [paper]

• Lifelong Graph Learning (CVPR2022) [paper]

• Lifelong Unsupervised Domain Adaptive Person Re-identification with Coordinated Anti-forgetting and Adaptation (CVPR2022) [paper]

• vCLIMB: A Novel Video Class Incremental Learning Benchmark (CVPR2022) [paper]

• Class-Incremental Learning by Knowledge Distillation with Adaptive Feature Consolidation(CVPR2022) [paper]

• Few-Shot Incremental Learning for Label-to-Image Translation (CVPR2022) [paper]

• MetaFSCIL: A Meta-Learning Approach for Few-Shot Class Incremental Learning (CVPR2022) [paper]

• Incremental Learning in Semantic Segmentation from Image Labels (CVPR2022) [paper]

• Self-Supervised Models are Continual Learners (CVPR2022) [paper] [code]

• Learning to Imagine: Diversify Memory for Incremental Learning using Unlabeled Data (CVPR2022) [paper]

• General Incremental Learning with Domain-aware Categorical Representations (CVPR2022) [paper]

• Constrained Few-shot Class-incremental Learning (CVPR2022) [paper]

• Overcoming Catastrophic Forgetting in Incremental Object Detection via Elastic Response Distillation (CVPR2022) [paper]

• Class-Incremental Learning with Strong Pre-trained Models (CVPR2022) [paper]

• Energy-based Latent Aligner for Incremental Learning (CVPR2022) [paper] [code]

• Meta-attention for ViT-backed Continual Learning (CVPR2022) [paper] [code]

• Learning to Prompt for Continual Learning (CVPR2022) [paper] [code]

• On Generalizing Beyond Domains in Cross-Domain Continual Learning (CVPR2022) [paper]

• Probing Representation Forgetting in Supervised and Unsupervised Continual Learning (CVPR2022) [paper]

• Incremental Transformer Structure Enhanced Image Inpainting with Masking Positional Encoding (CVPR2022) [paper] [code]

• Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning (CVPR2022) [paper] [code]

• Forward Compatible Few-Shot Class-Incremental Learning (CVPR2022) [paper] [code]

• Self-Sustaining Representation Expansion for Non-Exemplar Class-Incremental Learning (CVPR2022) [paper]

• DyTox: Transformers for Continual Learning with DYnamic TOken eXpansion (CVPR2022) [paper]

• Federated Class-Incremental Learning (CVPR2022) [paper] [code]

• Representation Compensation Networks for Continual Semantic Segmentation (CVPR2022) [paper]

• A Multi-Head Model for Continual Learning via Out-of-Distribution Replay (CoLLAs2022) [paper] [code]

• Continual Attentive Fusion for Incremental Learning in Semantic Segmentation (TMM2022) [paper]

• Self-training for class-incremental semantic segmentation (TNNLS2022) [paper]

• Effects of Auxiliary Knowledge on Continual Learning (ICPR2022) [paper]

• Continual Sequence Generation with Adaptive Compositional Modules (ACL2022) [paper]

• Learngene: From Open-World to Your Learning Task (AAAI2022) [paper] [code]

• Rethinking the Representational Continuity: Towards Unsupervised Continual Learning (ICLR2022) [paper]

• Continual Learning with Filter Atom Swapping (ICLR2022) [paper]

• Continual Learning with Recursive Gradient Optimization (ICLR2022) [paper]

• TRGP: Trust Region Gradient Projection for Continual Learning (ICLR2022) [paper]

• Looking Back on Learned Experiences For Class/task Incremental Learning (ICLR2022) [paper]

• Continual Normalization: Rethinking Batch Normalization for Online Continual Learning (ICLR2022) [paper]

• Model Zoo: A Growing Brain That Learns Continually (ICLR2022) [paper]

• Learning curves for continual learning in neural networks: Self-knowledge transfer and forgetting (ICLR2022) [paper]

• Memory Replay with Data Compression for Continual Learning (ICLR2022) [paper]

• Learning Fast, Learning Slow: A General Continual Learning Method based on Complementary Learning System (ICLR2022) [paper]

• Online Coreset Selection for Rehearsal-based Continual Learning (ICLR2022) [paper]

• Pretrained Language Model in Continual Learning: A Comparative Study (ICLR2022) [paper]

• Online Continual Learning on Class Incremental Blurry Task Configuration with Anytime Inference (ICLR2022) [paper]

• New Insights on Reducing Abrupt Representation Change in Online Continual Learning (ICLR2022) [paper]

• Towards Continual Knowledge Learning of Language Models (ICLR2022) [paper]

• CLEVA-Compass: A Continual Learning Evaluation Assessment Compass to Promote Research Transparency and Comparability (ICLR2022) [paper]

• CoMPS: Continual Meta Policy Search (ICLR2022) [paper]

• Information-theoretic Online Memory Selection for Continual Learning (ICLR2022) [paper]

• Subspace Regularizers for Few-Shot Class Incremental Learning (ICLR2022) [paper]

• LFPT5: A Unified Framework for Lifelong Few-shot Language Learning Based on Prompt Tuning of T5 (ICLR2022) [paper]

• Effect of scale on catastrophic forgetting in neural networks (ICLR2022) [paper]

• Dataset Knowledge Transfer for Class-Incremental Learning without Memory (WACV2022) [paper]

• Knowledge Capture and Replay for Continual Learning (WACV2022) [paper]

• Online Continual Learning via Candidates Voting (WACV2022) [paper]

• lpSpikeCon: Enabling Low-Precision Spiking Neural Network Processing for Efficient Unsupervised Continual Learning on Autonomous Agents (IJCNN2022) [paper]

• Unified Probabilistic Deep Continual Learning through Generative Replay and Open Set Recognition (Journal of Imaging 2022) [paper]

2021

• Incremental Object Detection via Meta-Learning (TPAMI 2021) [paper] [code]
• Triple-Memory Networks: A Brain-Inspired Method for Continual Learning (TNNLS 2021) [paper]
• Memory efficient class-incremental learning for image classification (TNNLS 2021) [paper]
• A Procedural World Generation Framework for Systematic Evaluation of Continual Learning (NeurIPS2021) [paper]
• Class-Incremental Learning via Dual Augmentation (NeurIPS2021) [paper]
• SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning (NeurIPS2021) [paper]
• RMM: Reinforced Memory Management for Class-Incremental Learning (NeurIPS2021) [paper]
• Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima (NeurIPS2021) [paper]
• Lifelong Domain Adaptation via Consolidated Internal Distribution (NeurIPS2021) [paper]
• AFEC: Active Forgetting of Negative Transfer in Continual Learning (NeurIPS2021) [paper]
• Natural continual learning: success is a journey, not (just) a destination (NeurIPS2021) [paper]
• Gradient-based Editing of Memory Examples for Online Task-free Continual Learning (NeurIPS2021) [paper]
• Optimizing Reusable Knowledge for Continual Learning via Metalearning (NeurIPS2021) [paper]
• Formalizing the Generalization-Forgetting Trade-off in Continual Learning (NeurIPS2021) [paper]
• Learning where to learn: Gradient sparsity in meta and continual learning (NeurIPS2021) [paper]
• Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning (NeurIPS2021) [paper]
• Posterior Meta-Replay for Continual Learning (NeurIPS2021) [paper]
• Continual Auxiliary Task Learning (NeurIPS2021) [paper]
• Mitigating Forgetting in Online Continual Learning with Neuron Calibration (NeurIPS2021) [paper]
• BNS: Building Network Structures Dynamically for Continual Learning (NeurIPS2021) [paper]
• DualNet: Continual Learning, Fast and Slow (NeurIPS2021) [paper]
• BooVAE: Boosting Approach for Continual Learning of VAE (NeurIPS2021) [paper]
• Generative vs. Discriminative: Rethinking The Meta-Continual Learning (NeurIPS2021) [paper]
• Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning (NeurIPS2021) [paper]
• Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection (NeurIPS, 2021) [paper] [code]
• SS-IL: Separated Softmax for Incremental Learning (ICCV, 2021) [paper]
• Striking a Balance between Stability and Plasticity for Class-Incremental Learning (ICCV, 2021) [paper]
• Synthesized Feature based Few-Shot Class-Incremental Learning on a Mixture of Subspaces (ICCV, 2021) [paper]
• Class-Incremental Learning for Action Recognition in Videos (ICCV, 2021) [paper]
• Continual Prototype Evolution:Learning Online from Non-Stationary Data Streams (ICCV, 2021) [paper]
• Rehearsal Revealed: The Limits and Merits of Revisiting Samples in Continual Learning (ICCV, 2021) [paper]
• Co2L: Contrastive Continual Learning (ICCV, 2021) [paper]
• Wanderlust: Online Continual Object Detection in the Real World (ICCV, 2021) [paper]
• Continual Learning on Noisy Data Streams via Self-Purified Replay (ICCV, 2021) [paper]
• Else-Net: Elastic Semantic Network for Continual Action Recognition from Skeleton Data (ICCV, 2021) [paper]
• Detection and Continual Learning of Novel Face Presentation Attacks (ICCV, 2021) [paper]
• Online Continual Learning with Natural Distribution Shifts: An Empirical Study with Visual Data (ICCV, 2021) [paper]
• Continual Learning for Image-Based Camera Localization (ICCV, 2021) [paper]
• Generalized and Incremental Few-Shot Learning by Explicit Learning and Calibration without Forgetting (ICCV, 2021) [paper]
• Always Be Dreaming: A New Approach for Data-Free Class-Incremental Learning (ICCV, 2021) [paper]
• RECALL: Replay-based Continual Learning in Semantic Segmentation (ICCV, 2021) [paper]
• Few-Shot and Continual Learning with Attentive Independent Mechanisms (ICCV, 2021) [paper]
• Learning with Selective Forgetting (IJCAI, 2021) [paper]
• Continuous Coordination As a Realistic Scenario for Lifelong Learning (ICML, 2021) [paper]
• Kernel Continual Learning (ICML, 2021) [paper]
• Variational Auto-Regressive Gaussian Processes for Continual Learning (ICML, 2021) [paper]
• Bayesian Structural Adaptation for Continual Learning (ICML, 2021) [paper]
• Continual Learning in the Teacher-Student Setup: Impact of Task Similarity (ICML, 2021) [paper]
• Continuous Coordination As a Realistic Scenario for Lifelong Learning (ICML, 2021) [paper]
• Federated Continual Learning with Weighted Inter-client Transfer (ICML, 2021) [paper]
• Adapting BERT for Continual Learning of a Sequence of Aspect Sentiment Classification Tasks (NAACL, 2021) [paper]
• Continual Learning for Text Classification with Information Disentanglement Based Regularization (NAACL, 2021) [paper]
• CLASSIC: Continual and Contrastive Learning of Aspect Sentiment Classification Tasks (EMNLP, 2021) [paper][code]
• Co-Transport for Class-Incremental Learning (ACM MM, 2021) [paper]
• Towards Open World Object Detection (CVPR, 2021) [paper] [code] [video]
• Prototype Augmentation and Self-Supervision for Incremental Learning (CVPR, 2021) [paper] [code]
• ORDisCo: Effective and Efficient Usage of Incremental Unlabeled Data for Semi-supervised Continual Learning (CVPR, 2021) [paper]
• Incremental Learning via Rate Reduction (CVPR, 2021) [paper]
• IIRC: Incremental Implicitly-Refined Classification (CVPR, 2021) [paper]
• Continual Adaptation of Visual Representations via Domain Randomization and Meta-learning (CVPR, 2021) [paper]
• Image De-raining via Continual Learning (CVPR, 2021) [paper]
• Continual Learning via Bit-Level Information Preserving (CVPR, 2021) [paper]
• Hyper-LifelongGAN: Scalable Lifelong Learning for Image Conditioned Generation (CVPR, 2021) [paper]
• Lifelong Person Re-Identification via Adaptive Knowledge Accumulation (CVPR, 2021) [paper]
• Distilling Causal Effect of Data in Class-Incremental Learning (CVPR, 2021) [paper]
• Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning (CVPR, 2021) [paper]
• Layerwise Optimization by Gradient Decomposition for Continual Learning (CVPR, 2021) [paper]
• Adaptive Aggregation Networks for Class-Incremental Learning (CVPR, 2021) [paper]
• Incremental Few-Shot Instance Segmentation (CVPR, 2021) [paper]
• Efficient Feature Transformations for Discriminative and Generative Continual Learning (CVPR, 2021) [paper]
• On Learning the Geodesic Path for Incremental Learning (CVPR, 2021) [paper]
• Few-Shot Incremental Learning with Continually Evolved Classifiers (CVPR, 2021) [paper]
• Rectification-based Knowledge Retention for Continual Learning (CVPR, 2021) [paper]
• DER: Dynamically Expandable Representation for Class Incremental Learning (CVPR, 2021) [paper]
• Rainbow Memory: Continual Learning with a Memory of Diverse Samples (CVPR, 2021) [paper]
• Training Networks in Null Space of Feature Covariance for Continual Learning (CVPR, 2021) [paper]
• Semantic-aware Knowledge Distillation for Few-Shot Class-Incremental Learning (CVPR, 2021) [paper]
• PLOP: Learning without Forgetting for Continual Semantic Segmentation (CVPR, 2021) [paper]
• Continual Semantic Segmentation via Repulsion-Attraction of Sparse and Disentangled Latent Representations (CVPR, 2021) [paper]
• Online Class-Incremental Continual Learning with Adversarial Shapley Value(AAAI, 2021) [paper] [code]
• Lifelong and Continual Learning Dialogue Systems: Learning during Conversation(AAAI, 2021) [paper]
• Continual learning for named entity recognition(AAAI, 2021) [paper]
• Using Hindsight to Anchor Past Knowledge in Continual Learning(AAAI, 2021) [paper]
• Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural Network(AAAI, 2021) [paper] [code]
• Curriculum-Meta Learning for Order-Robust Continual Relation Extraction(AAAI, 2021) [paper]
• Continual Learning by Using Information of Each Class Holistically(AAAI, 2021) [paper]
• Gradient Regularized Contrastive Learning for Continual Domain Adaptation(AAAI, 2021) [paper]
• Unsupervised Model Adaptation for Continual Semantic Segmentation(AAAI, 2021) [paper]
• A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation(AAAI, 2021) [paper]
• Do Not Forget to Attend to Uncertainty While Mitigating Catastrophic Forgetting(WACV, 2021) [paper]
• SpikeDyn: A Framework for Energy-Efficient Spiking Neural Networks with Continual and Unsupervised Learning Capabilities in Dynamic Environments (DAC2021) [paper]

2020

• Rethinking Experience Replay: a Bag of Tricks for Continual Learning(ICPR, 2020) [paper] [code]
• Continual Learning for Natural Language Generation in Task-oriented Dialog Systems(EMNLP, 2020) [paper]
• Distill and Replay for Continual Language Learning(COLING, 2020) [paper]
• Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks (NeurIPS2020) [paper] [code]
• Meta-Consolidation for Continual Learning (NeurIPS2020) [paper]
• Understanding the Role of Training Regimes in Continual Learning (NeurIPS2020) [paper]
• Continual Learning with Node-Importance based Adaptive Group Sparse Regularization (NeurIPS2020) [paper]
• Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning (NeurIPS2020) [paper]
• Coresets via Bilevel Optimization for Continual Learning and Streaming (NeurIPS2020) [paper]
• RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning (NeurIPS2020) [paper]
• Continual Deep Learning by Functional Regularisation of Memorable Past (NeurIPS2020) [paper]
• Dark Experience for General Continual Learning: a Strong, Simple Baseline (NeurIPS2020) [paper] [code]
• GAN Memory with No Forgetting (NeurIPS2020) [paper]
• Calibrating CNNs for Lifelong Learning (NeurIPS2020) [paper]
• Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization (NeurIPS2020) [paper]
• ADER: Adaptively Distilled Exemplar Replay Towards Continual Learning for Session-based Recommendation(RecSys, 2020) [paper]
• Initial Classifier Weights Replay for Memoryless Class Incremental Learning (BMVC2020) [paper]
• Adversarial Continual Learning (ECCV2020) [paper] [code]
• REMIND Your Neural Network to Prevent Catastrophic Forgetting (ECCV2020) [paper] [code]
• Incremental Meta-Learning via Indirect Discriminant Alignment (ECCV2020) [paper]
• Memory-Efficient Incremental Learning Through Feature Adaptation (ECCV2020) [paper]
• PODNet: Pooled Outputs Distillation for Small-Tasks Incremental Learning (ECCV2020) [paper] [code]
• Reparameterizing Convolutions for Incremental Multi-Task Learning Without Task Interference (ECCV2020) [paper]
• Learning latent representions across multiple data domains using Lifelong VAEGAN (ECCV2020) [paper]
• Online Continual Learning under Extreme Memory Constraints (ECCV2020) [paper]
• Class-Incremental Domain Adaptation (ECCV2020) [paper]
• More Classifiers, Less Forgetting: A Generic Multi-classifier Paradigm for Incremental Learning (ECCV2020) [paper]
• Piggyback GAN: Efficient Lifelong Learning for Image Conditioned Generation (ECCV2020) [paper]
• GDumb: A Simple Approach that Questions Our Progress in Continual Learning (ECCV2020) [paper]
• Imbalanced Continual Learning with Partitioning Reservoir Sampling (ECCV2020) [paper]
• Topology-Preserving Class-Incremental Learning (ECCV2020) [paper]
• GraphSAIL: Graph Structure Aware Incremental Learning for Recommender Systems (CIKM2020) [paper]
• OvA-INN: Continual Learning with Invertible Neural Networks (IJCNN2020) [paper]
• XtarNet: Learning to Extract Task-Adaptive Representation for Incremental Few-Shot Learning (ICLM2020) [paper]
• Optimal Continual Learning has Perfect Memory and is NP-HARD (ICML2020) [paper]
• Neural Topic Modeling with Continual Lifelong Learning (ICML2020) [paper]
• Continual Learning with Knowledge Transfer for Sentiment Classification (ECML-PKDD2020) [paper] [code]
• Semantic Drift Compensation for Class-Incremental Learning (CVPR2020) [paper] [code]
• Few-Shot Class-Incremental Learning (CVPR2020) [paper]
• Modeling the Background for Incremental Learning in Semantic Segmentation (CVPR2020) [paper]
• Incremental Few-Shot Object Detection (CVPR2020) [paper]
• Incremental Learning In Online Scenario (CVPR2020) [paper]
• Maintaining Discrimination and Fairness in Class Incremental Learning (CVPR2020) [paper]
• Conditional Channel Gated Networks for Task-Aware Continual Learning (CVPR2020) [paper]
• Continual Learning with Extended Kronecker-factored Approximate Curvature (CVPR2020) [paper]
• iTAML : An Incremental Task-Agnostic Meta-learning Approach (CVPR2020) [paper] [code]
• Mnemonics Training: Multi-Class Incremental Learning without Forgetting (CVPR2020) [paper] [code]
• ScaIL: Classifier Weights Scaling for Class Incremental Learning (WACV2020) [paper]
• Accepted papers(ICLR2020) [paper]
• Brain-inspired replay for continual learning with artificial neural networks (Natrue Communications 2020) [paper] [code]
• Learning to Continually Learn (ECAI 2020) [paper] [code]

2019

• Compacting, Picking and Growing for Unforgetting Continual Learning (NeurIPS2019)[paper][code]
• Increasingly Packing Multiple Facial-Informatics Modules in A Unified Deep-Learning Model via Lifelong Learning (ICMR2019) [paper][code]
• Towards Training Recurrent Neural Networks for Lifelong Learning (Neural Computation 2019) [paper]
• Complementary Learning for Overcoming Catastrophic Forgetting Using Experience Replay (IJCAI2019) [paper]
• IL2M: Class Incremental Learning With Dual Memory (ICCV2019) [paper]
• Incremental Learning Using Conditional Adversarial Networks (ICCV2019) [paper]
• Adaptive Deep Models for Incremental Learning: Considering Capacity Scalability and Sustainability (KDD2019) [paper]
• Random Path Selection for Incremental Learning (NeurIPS2019) [paper]
• Online Continual Learning with Maximal Interfered Retrieval (NeurIPS2019) [paper]
• Meta-Learning Representations for Continual Learning (NeurIPS2019) [paper] [code]
• Overcoming Catastrophic Forgetting with Unlabeled Data in the Wild (ICCV2019) [paper]
• Continual Learning by Asymmetric Loss Approximation with Single-Side Overestimation (ICCV2019) [paper]
• Lifelong GAN: Continual Learning for Conditional Image Generation (ICCV2019) [paper]
• Continual learning of context-dependent processing in neural networks (Nature Machine Intelligence 2019) [paper] [code]
• Large Scale Incremental Learning (CVPR2019) [paper] [code]
• Learning a Unified Classifier Incrementally via Rebalancing (CVPR2019) [paper] [code]
• Learning Without Memorizing (CVPR2019) [paper]
• Learning to Remember: A Synaptic Plasticity Driven Framework for Continual Learning (CVPR2019) [paper]
• Task-Free Continual Learning (CVPR2019) [paper]
• Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting (ICML2019) [paper]
• Efficient Lifelong Learning with A-GEM (ICLR2019) [paper] [code]
• Learning to Learn without Forgetting By Maximizing Transfer and Minimizing Interference (ICLR2019) [paper] [code]
• Overcoming Catastrophic Forgetting via Model Adaptation (ICLR2019) [paper]
• A comprehensive, application-oriented study of catastrophic forgetting in DNNs (ICLR2019) [paper]

2018

• Memory Replay GANs: learning to generate images from new categories without forgetting (NIPS2018) [paper] [code]
• Reinforced Continual Learning (NIPS2018) [paper] [code]
• Online Structured Laplace Approximations for Overcoming Catastrophic Forgetting (NIPS2018) [paper]
• Rotate your Networks: Better Weight Consolidation and Less Catastrophic Forgetting (R-EWC) (ICPR2018) [paper] [code]
• Exemplar-Supported Generative Reproduction for Class Incremental Learning (BMVC2018) [paper] [code]
• End-to-End Incremental Learning (ECCV2018) [paper][code]
• Riemannian Walk for Incremental Learning: Understanding Forgetting and Intransigence (ECCV2018)[paper]
• Piggyback: Adapting a Single Network to Multiple Tasks by Learning to Mask Weights (ECCV2018) [paper] [code]
• Memory Aware Synapses: Learning what (not) to forget (ECCV2018) [paper] [code]
• Lifelong Learning via Progressive Distillation and Retrospection (ECCV2018) [paper]
• PackNet: Adding Multiple Tasks to a Single Network by Iterative Pruning (CVPR2018) [paper] [code]
• Overcoming Catastrophic Forgetting with Hard Attention to the Task (ICML2018) [paper] [code]
• Lifelong Learning with Dynamically Expandable Networks (ICLR2018) [paper]
• FearNet: Brain-Inspired Model for Incremental Learning (ICLR2018) [paper]

2017

• Incremental Learning of Object Detectors Without Catastrophic Forgetting (ICCV2017) [paper]
• Overcoming catastrophic forgetting in neural networks (EWC) (PNAS2017) [paper] [code] [code]
• Continual Learning Through Synaptic Intelligence (ICML2017) [paper] [code]
• Gradient Episodic Memory for Continual Learning (NIPS2017) [paper] [code]
• iCaRL: Incremental Classifier and Representation Learning (CVPR2017) [paper] [code]
• Continual Learning with Deep Generative Replay (NIPS2017) [paper] [code]
• Overcoming Catastrophic Forgetting by Incremental Moment Matching (NIPS2017) [paper] [code]
• Expert Gate: Lifelong Learning with a Network of Experts (CVPR2017) [paper]
• Encoder Based Lifelong Learning (ICCV2017) [paper]

2016

• Learning without forgetting (ECCV2016) [paper] [code]

Awesome Long-Tailed Recognition / Imbalanced Learning

Find it interesting that there are more shared techniques than I thought for incremental learning (exemplars-based).

ContinualAI wiki

An Open Community of Researchers and Enthusiasts on Continual/Lifelong Learning for AI

Workshops

4th Lifelong Learning Workshop at ICML 2020

Workshop on Continual Learning at ICML 2020

Continual Learning in Computer Vision Workshop CVPR 2020

Continual learning workshop NeurIPS 2018

Challenges or Competitions

1st Lifelong Learning for Machine Translation Shared Task at WMT20 (EMNLP 2020)

Continual Learning in Computer Vision Challenge CVPR 2020

Lifelong Robotic Vision Challenge IROS 2019

Feel free to contact me if you find any interesting paper is missing.

Workshop papers are currently out due to space.