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Class Incremental Learning

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하위 태스크: 3
평가표: 6
보존된 평가 기록: 14

Incremental learning of a sequence of tasks when the task-ID is not available at test time.

코드

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

https://github.com/zhoujiahuan1991/aaai2025-caprompt

공식 코드

CAPrompt: Cyclic Prompt Aggregation for Pre-Trained Model Based Class Incremental Learning

https://github.com/zhu-gl-ux/adapter

공식 코드

Adaptive Prototype Replay for Class Incremental Semantic Segmentation

https://github.com/zhukaii/SPPR

공식 코드

Self-Promoted Prototype Refinement for Few-Shot Class-Incremental Learning

https://github.com/zkzhang98/coinseg

공식 코드

CoinSeg: Contrast Inter- and Intra- Class Representations for Incremental Segmentation

https://github.com/zkzhang98/microseg

공식 코드

Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation

https://github.com/zoilsen/clom

공식 코드

Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation

https://github.com/zoilsen/comp-fscil

공식 코드

Compositional Few-Shot Class-Incremental Learning

https://github.com/zqiao11/tscil

공식 코드

Class-incremental Learning for Time Series: Benchmark and Evaluation

https://github.com/zysong0113/savc

공식 코드

Learning with Fantasy: Semantic-Aware Virtual Contrastive Constraint for Few-Shot Class-Incremental Learning

https://github.com/zyuh/bdr-main

공식 코드

Balanced Destruction-Reconstruction Dynamics for Memory-replay Class Incremental Learning

https://github.com/zzz47zzz/codebase-for-incremental-learning-with-llm

공식 코드

Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models

https://github.com/zzz47zzz/pretrained-lm-for-incremental-learning

공식 코드

Learn or Recall? Revisiting Incremental Learning with Pre-trained Language Models

https://gitlab.com/viper-purdue/ocil-real-world-food-image-classification

공식 코드

Online Class-Incremental Learning For Real-World Food Image Classification

https://gitlab.mpi-klsb.mpg.de/yaoyaoliu/rmm

공식 코드

RMM: Reinforced Memory Management for Class-Incremental Learning

https://gitlab.mpi-klsb.mpg.de/yaoyaoliu/rmm/

공식 코드

RMM: Reinforced Memory Management for Class-Incremental Learning

https://github.com/2023-MindSpore-1/ms-code-70

Learning Adaptive Embedding Considering Incremental Class

https://github.com/DRSAD/iCaRL

iCaRL: Incremental Classifier and Representation Learning

https://github.com/GMvandeVen/complex-synapses

Three scenarios for continual learning

https://github.com/GuoQuanhao/SupContrast-Paddle

Supervised Contrastive Learning

https://github.com/JH-LEE-KR/l2p-pytorch

Learning to Prompt for Continual Learning

https://github.com/Kabir5296/CSTA-CustomImplementation

CSTA: Spatial-Temporal Causal Adaptive Learning for Exemplar-Free Video Class-Incremental Learning

https://github.com/King-Of-Knights/overcoming-catastrophic

Overcoming catastrophic forgetting in neural networks

https://github.com/LTTM/IL-SemSegm

Incremental Learning Techniques for Semantic Segmentation

https://github.com/Liut2016/ecg-supcontrast

Supervised Contrastive Learning

https://github.com/MECLabTUDA/ACS

Incremental Learning Techniques for Semantic Segmentation

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/PaddlePaddle/PaddleClas

Supervised Contrastive Learning

https://github.com/PaperCodeReview/SupCL-TF

Supervised Contrastive Learning

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

Three scenarios for continual learning

https://github.com/XG293/SupConLoss

Supervised Contrastive 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/ZIZUN/CPFT

Supervised Contrastive Learning

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