https://github.com/vinuni-vishc/cg-imif
공식 코드Multi-stream Fusion for Class Incremental Learning in Pill Image Classification
Papers with Code 아카이브
Class Incremental Learning
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 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/vita-qzh/apart
공식 코드Adaptive Adapter Routing for Long-Tailed Class-Incremental Learning
https://github.com/vt-nlp/ice
공식 코드https://github.com/wangkiw/iclr23-memo
공식 코드A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning
https://github.com/wangkiw/teen
공식 코드Few-Shot Class-Incremental Learning via Training-Free Prototype Calibration
https://github.com/weiguopian/av-cil_iccv2023
공식 코드https://github.com/witdsl/krt-mlcil
공식 코드Knowledge Restore and Transfer for Multi-label Class-Incremental Learning
https://github.com/wonseok1017/nn-rfr
공식 코드https://github.com/xfflzl/cim-cil
공식 코드Class-Incremental Exemplar Compression for Class-Incremental Learning
https://github.com/xialeiliu/GFR-IL
공식 코드https://github.com/xialeiliu/long-tailed-cil
공식 코드https://github.com/xiaojieli0903/ckpd-fscil
공식 코드Continuous Knowledge-Preserving Decomposition for Few-Shot Continual Learning
https://github.com/xiaojieli0903/cpkd_fscil
공식 코드Continuous Knowledge-Preserving Decomposition for Few-Shot Continual Learning
https://github.com/xiaojieli0903/mamba-fscil
공식 코드https://github.com/xmengxin/mfgr
공식 코드Memory-Free Generative Replay For Class-Incremental Learning
https://github.com/yaoyao-liu/class-incremental-learning
공식 코드Adaptive Aggregation Networks for Class-Incremental Learning
https://github.com/yaoyao-liu/mnemonics
공식 코드Mnemonics Training: Multi-Class Incremental Learning without Forgetting
https://github.com/yaoyao-liu/online-hyperparameter-optimization
공식 코드Online Hyperparameter Optimization for Class-Incremental Learning
https://github.com/yawencui/uad-ce
공식 코드Uncertainty-Aware Distillation for Semi-Supervised Few-Shot Class-Incremental Learning
https://github.com/yellowpancake/tcil
공식 코드Resolving Task Confusion in Dynamic Expansion Architectures for Class Incremental Learning
https://github.com/yezilaixi/kt-rcnet
공식 코드Knowledge Transfer-Driven Few-Shot Class-Incremental Learning
https://github.com/yhchen12101/FGP-ICL
공식 코드Class-incremental Learning with Rectified Feature-Graph Preservation
https://github.com/yongzhang-tan/adbs
공식 코드Adaptive Decision Boundary for Few-Shot Class-Incremental Learning
https://github.com/yujun-shi/cwd
공식 코드Mimicking the Oracle: An Initial Phase Decorrelation Approach for Class Incremental Learning
https://github.com/yulu0724/SDC-IL
공식 코드https://github.com/zhang-jinghua/dbc-fscil
공식 코드A Forward and Backward Compatible Framework for Few-shot Class-incremental Pill Recognition
https://github.com/zhangchbin/rcil
공식 코드Representation Compensation Networks for Continual Semantic Segmentation
https://github.com/zhen-tan-dmml/gfcil
공식 코드https://github.com/zhengyuan-xie/eccv24_nest
공식 코드Early Preparation Pays Off: New Classifier Pre-tuning for Class Incremental Semantic Segmentation
https://github.com/zhihelu/ending_iss
공식 코드Evolving Knowledge Mining for Class Incremental Segmentation
https://github.com/zhoudw-zdw/MM21-Coil
공식 코드https://github.com/zhoudw-zdw/PROOF
공식 코드https://github.com/zhoudw-zdw/TPAMI-Limit
공식 코드Few-Shot Class-Incremental Learning by Sampling Multi-Phase Tasks
https://github.com/zhoudw-zdw/cil_survey
공식 코드A Model or 603 Exemplars: Towards Memory-Efficient Class-Incremental Learning
https://github.com/zhoudw-zdw/cvpr22-fact
공식 코드https://github.com/zhoudw-zdw/revisitingcil
공식 코드출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.