There are 31 repositories under continual-learning topic.
FedML - The Research and Production Integrated Federated Learning Library: https://fedml.ai
Avalanche: an End-to-End Library for Continual Learning based on PyTorch.
PyTorch implementation of various methods for continual learning (XdG, EWC, SI, LwF, FROMP, DGR, BI-R, ER, A-GEM, iCaRL, Generative Classifier) in three different scenarios.
collection of diffusion model papers categorized by their subareas
NMA deep learning course
Awesome Machine Unlearning (A Survey of Machine Unlearning)
Continual Learning papers list, curated by ContinualAI
Evaluate three types of task shifting with popular continual learning algorithms.
An Incremental Learning, Continual Learning, and Life-Long Learning Repository
PyTorch implementation of AANets (CVPR 2021) and Mnemonics Training (CVPR 2020 Oral)
Learning to Prompt (L2P) for Continual Learning @ CVPR22 and DualPrompt: Complementary Prompting for Rehearsal-free Continual Learning @ ECCV22
A collection of incremental learning paper implementations including PODNet (ECCV20) and Ghost (CVPR-W21).
A collection of online continual learning paper implementations and tricks for computer vision in PyTorch, including our ASER(AAAI-21), SCR(CVPR21-W) and an online continual learning survey (Neurocomputing).
Continual Learning tutorials and demo running on Google Colaboratory.
PyContinual (An Easy and Extendible Framework for Continual Learning)
Continual learning baselines and strategies from popular papers, using Avalanche. We include EWC, SI, GEM, AGEM, LwF, iCarl, GDumb, and other strategies.
A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).
🎉 PILOT: A Pre-trained Model-Based Continual Learning Toolbox
An Extensible Continual Learning Framework Focused on Language Models (LMs)
Universal User Representation Pre-training for Cross-domain Recommendation and User Profiling
The code repository for "Deep Class-Incremental Learning: A Survey" in PyTorch.
Dataset for MetaSLAM Challenge
PyTorch Implementation of Learning to Prompt (L2P) for Continual Learning @ CVPR22
A Comprehensive Survey of Forgetting in Deep Learning Beyond Continual Learning. arXiv:2307.09218.
Official code of CVPR 2021's PLOP: Learning without Forgetting for Continual Semantic Segmentation
Dynamic Token Expansion with Continual Transformers, accepted at CVPR 2022
Continual SLAM: Beyond Lifelong Simultaneous Localization and Mapping through Continual Learning. http://continual-slam.cs.uni-freiburg.de