Avalanche: an End-to-End Library for Continual Learning based on PyTorch.
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Updated
Jun 21, 2024 - Python
Avalanche: an End-to-End Library for Continual Learning based on PyTorch.
(CVPR 2021 Oral) Open World Object Detection
PyCIL: A Python Toolbox for Class-Incremental Learning
Framework for Analysis of Class-Incremental Learning with 12 state-of-the-art methods and 3 baselines.
Evaluate three types of task shifting with popular continual learning algorithms.
An Extendible (General) Continual Learning Framework based on Pytorch - official codebase of Dark Experience for General Continual Learning
PyTorch implementation of AANets (CVPR 2021) and Mnemonics Training (CVPR 2020 Oral)
A clean and simple data loading library for Continual Learning
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).
PyContinual (An Easy and Extendible Framework for Continual Learning)
Library for automatic retraining and continual learning
🎉 PILOT: A Pre-trained Model-Based Continual Learning Toolbox
Continual learning baselines and strategies from popular papers, using Avalanche. We include EWC, SI, GEM, AGEM, LwF, iCarl, GDumb, and other strategies.
An Extensible Continual Learning Framework Focused on Language Models (LMs)
A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).
Universal User Representation Pre-training for Cross-domain Recommendation and User Profiling
Continual Hyperparameter Selection Framework. Compares 11 state-of-the-art Lifelong Learning methods and 4 baselines. Official Codebase of "A continual learning survey: Defying forgetting in classification tasks." in IEEE TPAMI.
The code repository for "Deep Class-Incremental Learning: A Survey" in PyTorch.
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