https://github.com/wangermeng2021/Scaled-YOLOv4-tensorflow2
Sharpness-Aware Minimization for Efficiently Improving Generalization
Papers with Code 아카이브
Learning with noisy labels
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Learning with noisy labels means When we say "noisy labels," we mean that an adversary has intentionally messed up the labels, which would have come from a "clean" distribution otherwise. This setting can also be used to cast learning from only positive and unlabeled data.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/weijiaheng/Multi-class-Peer-Loss-functions
Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates
https://github.com/xingruiyu/coteaching_plus
How does Disagreement Help Generalization against Label Corruption?
https://github.com/yeachan-kr/pytorch-coteaching
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
https://github.com/yeachan-kr/pytorch-sigua
SIGUA: Forgetting May Make Learning with Noisy Labels More Robust
https://github.com/ys-zong/medfair
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/zhjscut/IterLNL
https://github.com/ziegler-ingo/cleavage_prediction
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.