https://github.com/dvirsamuel/DRAGON
공식 코드From Generalized zero-shot learning to long-tail with class descriptors
Papers with Code 아카이브
Long-tail learning with class descriptors
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
- 하위 태스크
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- 평가표
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- 보존된 평가 기록
- 20
Long-tail learning by using class descriptors (like attributes, class embedding, etc) to learn tail classes as well as head classes.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/facebookresearch/classifier-balancing
공식 코드Decoupling Representation and Classifier for Long-Tailed Recognition
https://github.com/kaidic/LDAM-DRW
공식 코드Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
https://github.com/shijxcs/lift
공식 코드Long-Tail Learning with Foundation Model: Heavy Fine-Tuning Hurts
https://github.com/PhilChina/classifier-balancing
Decoupling Representation and Classifier for Long-Tailed Recognition
https://github.com/edgarschnfld/CADA-VAE-PyTorch
https://github.com/feidfoe/AdjustBnd4Imbalance
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
https://github.com/ihaeyong/maximum-margin-ldam
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
https://github.com/j3soon/arxiv-utils
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
https://github.com/jackhck/subclass-balancing-contrastive-learning
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
https://github.com/karurb92/ldam_str_bn
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
https://github.com/orparask/VS-Loss
Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss
https://github.com/silicx/dlsa
https://github.com/zhmiao/OpenLongTailRecognition-OLTR
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.