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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/xiaojieli0903/mamba-fscil

공식 코드

Mamba-FSCIL: Dynamic Adaptation with Selective State Space Model for Few-Shot Class-Incremental Learning

https://github.com/xmengxin/mfgr

공식 코드

Memory-Free Generative Replay For Class-Incremental Learning

https://github.com/yaoyao-liu/class-incremental-learning

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Adaptive Aggregation Networks for Class-Incremental Learning

https://github.com/yaoyao-liu/mnemonics

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Mnemonics Training: Multi-Class Incremental Learning without Forgetting

https://github.com/yaoyao-liu/online-hyperparameter-optimization

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Online Hyperparameter Optimization for Class-Incremental Learning

https://github.com/yawencui/uad-ce

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Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning

https://github.com/yellowpancake/tcil

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Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning

https://github.com/yezilaixi/kt-rcnet

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Knowledge Transfer-Driven Few-Shot Class-Incremental Learning

https://github.com/yhchen12101/FGP-ICL

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Class-incremental Learning with Rectified Feature-Graph Preservation

https://github.com/yongzhang-tan/adbs

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Adaptive Decision Boundary for Few-Shot Class-Incremental Learning

https://github.com/yujun-shi/cwd

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Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning

https://github.com/yulu0724/SDC-IL

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Semantic Drift Compensation for Class-Incremental Learning

https://github.com/zhang-jinghua/dbc-fscil

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A Forward and Backward Compatible Framework for Few-shot Class-incremental Pill Recognition

https://github.com/zhen-tan-dmml/gfcil

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Graph Few-shot Class-incremental Learning

https://github.com/zhoudw-zdw/MM21-Coil

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Co-Transport for Class-Incremental Learning

https://github.com/zhoudw-zdw/PROOF

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Learning without Forgetting for Vision-Language Models

https://github.com/zhoudw-zdw/TPAMI-Limit

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Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks

https://github.com/zhoudw-zdw/cil_survey

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A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning

https://github.com/zhoudw-zdw/cvpr22-fact

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Forward Compatible Few-Shot Class-Incremental Learning

https://github.com/zhoudw-zdw/revisitingcil

공식 코드

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

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

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

https://github.com/zhukaii/SPPR

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

https://github.com/zoilsen/clom

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

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

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Compositional Few-Shot Class-Incremental Learning

https://github.com/zqiao11/tscil

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Class-incremental Learning for Time Series: Benchmark and Evaluation

https://github.com/zysong0113/savc

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

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

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

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

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

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

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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

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