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class-incremental learning

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코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/juntingzh/incremental-learning-baselines

Class-incremental Learning via Deep Model Consolidation

https://github.com/kamsyn95/CL_DNN

Overcoming catastrophic forgetting in neural networks

https://github.com/kimjeyoung/Mahalanobis-BERT

A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

https://github.com/kuc2477/pytorch-deep-generative-replay

Continual Learning with Deep Generative Replay

https://github.com/lamda-cl/revisitingcil

Revisiting Class-Incremental Learning with Pre-Trained Models: Generalizability and Adaptivity are All You Need

https://github.com/lancopku/avg-avg

A Simple Unified Framework for Detecting Out-of-Distribution Samples and Adversarial Attacks

https://github.com/llzlcl/Continual-Learning

Three scenarios for continual learning

https://github.com/lrzpellegrini/CL-CORe-App

Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches

https://github.com/lrzpellegrini/Fine-Grained-Continual-Learning

Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches

https://github.com/lrzpellegrini/avalanche

Avalanche: an End-to-End Library for Continual Learning

https://github.com/mindspore-ai/contrib/blob/master/application/EWC-Overcoming-catastrophic-forgetting/demo.ipynb

Overcoming catastrophic forgetting in neural networks

https://github.com/mu-y/lll-tts

Overcoming catastrophic forgetting in neural networks

https://github.com/neuraptic/cpe-clip

Multimodal Parameter-Efficient Few-Shot Class Incremental Learning

https://github.com/nvshrao/Elastic-Weight-Consolidation

Overcoming catastrophic forgetting in neural networks

https://github.com/rokmr/TACLE

TACLE: Task and Class-aware Exemplar-free Semi-supervised Class Incremental Learning

https://github.com/sairin1202/BIC

Large Scale Incremental Learning

https://github.com/shivamsaboo17/overcoming-catastrophic-forgetting-in-neural-networks

Overcoming catastrophic forgetting in neural networks

https://github.com/sjtu-dmtai/onlinetsf

Dark Experience for General Continual Learning: a Strong, Simple Baseline

https://github.com/srvCodes/continual-learning-benchmark

Overcoming catastrophic forgetting in neural networks

https://github.com/stijani/elastic-weight-consolidation-tf2

Overcoming catastrophic forgetting in neural networks

https://github.com/vlomonaco/ar1-pytorch

Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches

https://github.com/vlomonaco/core50

Rehearsal-Free Continual Learning over Small Non-I.I.D. Batches

https://github.com/wannaa/c-flat

Maintaining Discrimination and Fairness in Class Incremental Learning

https://github.com/wannabeOG/MAS-PyTorch

Overcoming catastrophic forgetting in neural networks

https://github.com/wjmacro/continualmt

Overcoming catastrophic forgetting in neural networks

https://github.com/wuzhiyuan2000/FedAgg

Federated Class-Incremental Learning with New-Class Augmented Self-Distillation

https://github.com/wyatuestc/overcoming-catastrophic

Overcoming catastrophic forgetting in neural networks

https://github.com/xduan7/hat-cl

Overcoming catastrophic forgetting in neural networks

https://github.com/xyutao/fscil

Few-Shot Class-Incremental Learning

https://github.com/yaqianzhang/repeatedaugmentedrehearsal

Supervised Contrastive Replay: Revisiting the Nearest Class Mean Classifier in Online Class-Incremental Continual Learning

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.