Popular GitHub Repositories for Lifelong

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.

For students, scholars, interviewees and lifelong learners. Let LLMs assist you in learning ?

PyCIL: A Python Toolbox for Class-Incremental Learning

Benchmarking Knowledge Transfer in Lifelong Robot 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.

? PILOT: A Pre-trained Model-Based Continual Learning Toolbox

VOSK Speech Recognition Toolkit

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).

A brain-inspired version of generative replay for continual learning with deep neural networks (e.g., class-incremental learning on CIFAR-100; PyTorch code).

生活纪律项目 (LDP) 是一个基于 Obsidian 笔记软件构建的生活管理框架。你可以根据自身需求随意调整它。

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.

This repository collects awesome survey, resource, and paper for lifelong learning LLM agents

Meta-Learning through Hebbian Plasticity in Random Networks: https://arxiv.org/abs/2007.02686

[ACM Computing Surveys 2025] This repository collects awesome survey, resource, and paper for Lifelong Learning with Large Language Models. (Updated Regularly)

Steven C. Y. Hung, Cheng-Hao Tu, Cheng-En Wu, Chien-Hung Chen, Yi-Ming Chan, and Chu-Song Chen, "Compacting, Picking and Growing for Unforgetting Continual Learning," Thirty-third Conference on Neural Information Processing Systems, NeurIPS 2019

AutoGBT is used for AutoML in a lifelong machine learning setting to classify large volume high cardinality data streams under concept-drift. AutoGBT was developed by a joint team ('autodidact.ai') from Flytxt, Indian Institute of Technology Delhi and CSIR-CEERI as a part of NIPS 2018 AutoML for Lifelong Machine Learning Challenge.