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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/MindCode-4/code-11/tree/main/EWC-Overcoming-catastrophic-forgetting

Overcoming catastrophic forgetting in neural networks

https://github.com/MindCode-4/code-6/tree/main/EWC-Overcoming-catastrophic-forgetting

Overcoming catastrophic forgetting in neural networks

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

Overcoming catastrophic forgetting in neural networks

https://github.com/PCJohn/cata-forget

Overcoming catastrophic forgetting in neural networks

https://github.com/WangTianduo/lifelong-learning-3-cases

Three scenarios for continual learning

https://github.com/XSMUBC/DNC-lifelong-learning

Three scenarios for continual learning

https://github.com/XSMUBC/Lifelong-learning_xsm

Three scenarios for continual learning

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

Overcoming catastrophic forgetting in neural networks

https://github.com/caotians1/OD-test-master

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

https://github.com/catid/never_forget

Overcoming catastrophic forgetting in neural networks

https://github.com/ccc-frankfurt/DP-NAS

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

https://github.com/chandar-lab/IIRC

IIRC: Incremental Implicitly-Refined Classification

https://github.com/clam004/intro_continual_learning

Overcoming catastrophic forgetting in neural networks

https://github.com/codelion/adaptive-classifier/blob/main/src/adaptive_classifier/ewc.py

Overcoming catastrophic forgetting in neural networks

https://github.com/continualai/avalanche-rl

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

https://github.com/dalxndr/NEURO140

Overcoming catastrophic forgetting in neural networks

https://github.com/daskol/paper-reviews

Overcoming catastrophic forgetting in neural networks

https://github.com/david-knigge/overcoming-catastrophic-forgetting-fncm-uva

Overcoming catastrophic forgetting in neural networks

https://github.com/donlee90/icarl

iCaRL: Incremental Classifier and Representation Learning

https://github.com/epsilon-deltta/ssd_guillotine

Continual Learning with Deep Generative Replay

https://github.com/ethz-asl/background_foreground_segmentation

Overcoming catastrophic forgetting in neural networks

https://github.com/fionisarnen/Lifelong-Learning-churn-prediction-torch

Overcoming catastrophic forgetting in neural networks

https://github.com/gahaalt/continual-learning-overview

Three scenarios for continual learning

https://github.com/gahaalt/continual-learning-with-hypernets

Three scenarios for continual learning

https://github.com/geox-lab/cmn

Continual Learning with Deep Generative Replay

https://github.com/gmum/hint

FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning

https://github.com/gmum/hyperinterval

FeCAM: Exploiting the Heterogeneity of Class Distributions in Exemplar-Free Continual Learning

https://github.com/gulzainali98/LGCL

Learning to Prompt for Continual Learning

https://github.com/haseebs/Pseudo-rehearsal-Incremental-Learning

iCaRL: Incremental Classifier and Representation Learning

https://github.com/hursung1/DeepGenerativeReplay

Continual Learning with Deep Generative Replay

https://github.com/icoz69/cec-cvpr2021

Few-Shot Incremental Learning with Continually Evolved Classifiers

https://github.com/iemprog/mimi

Rethinking Class-incremental Learning in the Era of Large Pre-trained Models via Test-Time Adaptation

https://github.com/ihaeyong/SoftNet-3DLS

On the Soft-Subnetwork for Few-shot Class Incremental Learning

https://github.com/ihaeyong/pnr

Continual Learning: Forget-free Winning Subnetworks for Video Representations

https://github.com/jiahuadong/fiss

No One Left Behind: Real-World Federated Class-Incremental Learning

https://github.com/jie-su/bpd

Energy-based Latent Aligner for Incremental Learning

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