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Awesome Incremental Learning

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Awesome Incremental Learning / Lifelong learning

Survey

  • Online Continual Learning in Image Classification: An Empirical Survey (arXiv 2020) [paper] [code]
  • Continual Lifelong Learning in Natural Language Processing: A Survey (COLING 2020) [paper]
  • Class-incremental learning: survey and performance evaluation (arXiv 2020) [paper]
  • A Comprehensive Study of Class Incremental Learning Algorithms for Visual Tasks (Neural Networks) [paper] [code]
  • Continual learning: A comparative study on how to defy forgetting in classification tasks (arXiv 2019) [paper]
  • Continual Lifelong Learning with Neural Networks: A Review (Neural Networks) [paper]

Papers

2021

  • Online Class-Incremental Continual Learning with Adversarial Shapley Value(AAAI, 2021) [paper] [code]

2020

  • Continual Learning for Natural Language Generation in Task-oriented Dialog Systems(EMNLP, 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]
  • 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]

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]
  • 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]

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

ContinualAI wiki

Workshops

Challenges or Competitions

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

Workshop papers are currently out due to space.

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Awesome Incremental Learning