(AAAI 2021) Split-and-Bridge: Adaptable Class Incremental Learning within a Single Neural Network

The code repository for "Co-Transport for Class-Incremental Learning" (ACM MM'21) in PyTorch.

Official PyTorch Implementation of PuriDivER CVPR 2022.

[TMLR 22] "Queried Unlabeled Data Improves and Robustifies Class- Incremental Learning" by Tianlong Chen, Sijia Liu, Shiyu Chang, Lisa Animi, Zhangyang Wang

a PyTorch Tutorial to Class-Incremental Learning | a Distributed Training Template of CIL with core code less than 100 lines.

Official Implementation of the ECCV 2022 Paper "Class-Incremental Learning with Cross-Space Clustering and Controlled Transfer"

Class Incremental Learning (iCaRL, EEIL, BiC) reproduce github repository.

[AAAI 2022 Oral] Static-Dynamic Co-Teaching for Class-Incremental 3D Object Detection

The official implementation for ECCV22 paper: "FOSTER: Feature Boosting and Compression for Class-Incremental Learning" in PyTorch.

Code for SCIENTIA SINICA Informationis paper "Generalized representation of local relationships for few-shot incremental learning", 局部关系泛化表征的小样本增量学习

PyTorch implementation of AANets (CVPR 2021) and Mnemonics Training (CVPR 2020 Oral)

PyTorch implementation of a VAE-based generative classifier, as well as other class-incremental learning methods that do not store data (DGR, BI-R, EWC, SI, CWR, CWR+, AR1, the "labels trick", SLDA).

Official Implementation of the paper "Exemplar-free Continual Learning of Vision Transformers via Gated Class-Attention and Cascaded Feature Drift Compensation"

The code repository for "A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning" (ICLR'23) in PyTorch

Adaptive Decision Forest(ADF) is an incremental machine learning framework called to produce a decision forest to classify new records. ADF is capable to classify new records even if they are associated with previously unseen classes. ADF also is capable of identifying and handling concept drift; it, however, does not forget previously gained kn…

The code repository for "Deep Class-Incremental Learning: A Survey" in PyTorch.

Code for the ICLR2022 paper on Subspace Regularization for few-shot class incremental image classification

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).

The official code for our paper "Neural Collapse Terminus: A Unified Solution for Class Incremental Learning and Its Variants".

[ICLR 2023] The official code for our ICLR 2023 (top25%) paper: "Neural Collapse Inspired Feature-Classifier Alignment for Few-Shot Class-Incremental Learning"