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Unsupervised Few-Shot Image Classification
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In contrast to (supervised) few-shot image classification, only the unlabeled dataset is available in the pre-training or meta-training stage for unsupervised few-shot image classification.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/WonderSeven/ULDA
공식 코드Diversity Helps: Unsupervised Few-shot Learning via Distribution Shift-based Data Augmentation
https://github.com/alinlab/psco
공식 코드Unsupervised Meta-Learning via Few-shot Pseudo-supervised Contrastive Learning
https://github.com/bbbdylan/unisiam
공식 코드https://github.com/guodongqi/cmvae
공식 코드https://github.com/hanlu-nju/revisiting-uml
공식 코드Revisiting Unsupervised Meta-Learning via the Characteristics of Few-Shot Tasks
https://github.com/hongguangzhang/mlso-tmm-master
공식 코드https://github.com/indy-lab/ProtoTransfer
공식 코드Self-Supervised Prototypical Transfer Learning for Few-Shot Classification
https://github.com/ojss/c3lr
공식 코드https://github.com/ojss/samptransfer
공식 코드Self-Attention Message Passing for Contrastive Few-Shot Learning
https://github.com/stypoumic/beclr
공식 코드https://github.com/xingpingdong/pl-cfe
공식 코드Rethinking Clustering-Based Pseudo-Labeling for Unsupervised Meta-Learning
https://github.com/2023-MindSpore-1/ms-code-217/tree/main/unisiam
https://github.com/Haoqing-Wang/CPNWCP
https://github.com/MindSpore-paper-code-2/code3/tree/main/unisiam
https://github.com/db-Lee/Meta-GMVAE
https://github.com/phecy/SSL-FEW-SHOT
https://github.com/xidianai/LF2CS
https://github.com/yangyucheng000/MSpaper/tree/main/unisiam
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.