Brain-Inspired Replay A PyTorch implementation of the continual learning experiments with deep neural networks described in the following paper: Brain

Incremental Object Detection via Meta-Learning To appear in an upcoming issue of the IEEE Transactions on Pattern Analysis and Machine Intelligence (T

Adam-NSCL This is a PyTorch implementation of Adam-NSCL algorithm for continual learning from our CVPR2021 (oral) paper: Title: Training Networks in N

PyContinual (An Easy and Extendible Framework for Continual Learning) Easy to Use You can sumply change the baseline, backbone and task, and then read

Mammoth - An Extendible (General) Continual Learning Framework for Pytorch NEWS STAY TUNED: We are working on an update of this repository to include

PLOP: Learning without Forgetting for Continual Semantic Segmentation This repository contains all of our code. It is a modified version of Cermelli e

Energy-based Latent Aligner for Incremental Learning Accepted to CVPR 2022 We illustrate an Incremental Learning model trained on a continuum of tasks

RCIL [CVPR2022] Representation Compensation Networks for Continual Semantic Segmentation Chang-Bin Zhang1, Jia-Wen Xiao1, Xialei Liu1, Ying-Cong Chen2

Learning to Prompt for Continual Learning (L2P) Official Jax Implementation L2P is a novel continual learning technique which learns to dynamically pr

Continual Learning of Longitudinal Health Records Repo for reproducing the experiments in Continual Learning of Longitudinal Health Records (2021). Re

Continual learning paper repository This repository contains an incomplete (but dynamically updated) list of papers exploring continual learning in ma

Online Continual Learning via Multiple Deep Metric Learning and Uncertainty-guided Episodic Memory Replay 3rd Place Solution for ICCV 2021 Workshop SS

Bayesian Deep Learning for Manufacturing 2.0 (dlmfg) Object Shape Error Response (OSER) Digital Lifecycle Management - In Process Quality Improvement

Official Pytorch Implementation for GLFC [CVPR-2022] Federated Class-Incremental Learning This is the official implementation code of our paper "Feder

Coresets via Bilevel Optimization This is the reference implementation for "Coresets via Bilevel Optimization for Continual Learning and Streaming" ht

PyContinual (An Easy and Extendible Framework for Continual Learning) Easy to Use You can sumply change the baseline, backbone and task, and then read