https://github.com/XinshaoAmosWang/Improving-Mean-Absolute-Error-against-CCE
Normalized Loss Functions for Deep Learning with Noisy Labels
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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/Yan107351111/SSOD
Learning to Detect and Retrieve Objects from Unlabeled Videos
https://github.com/Yuheon/Sharp-Aware-Minimization
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/ansuini/IntrinsicDimDeep
https://github.com/arghosh/noisy_label_pretrain
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
https://github.com/arpit2412/InstanceGM
Instance-Dependent Noisy Label Learning via Graphical Modelling
https://github.com/awasthiabhijeet/Learning-From-Rules
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
https://github.com/bhanML/coteaching_plus
How does Disagreement Help Generalization against Label Corruption?
https://github.com/borealisai/perturbed-forgetting
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/cgnorthcutt/confidentlearning-reproduce
Confident Learning: Estimating Uncertainty in Dataset Labels
https://github.com/chang-yue/ctrl
Confident Learning: Estimating Uncertainty in Dataset Labels
https://github.com/chenghui-666/acl
Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction
https://github.com/cleanlab/examples
Confident Learning: Estimating Uncertainty in Dataset Labels
https://github.com/codelion/adaptive-classifier/blob/main/src/adaptive_classifier/memory.py
https://github.com/davda54/sam
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/debajyotid2/noisy_prediction_calibration
From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model
https://github.com/denizyuret/playground
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/dmizr/phuber
Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels
https://github.com/hanxunh/sceloss-reproduce
Symmetric Cross Entropy for Robust Learning with Noisy Labels
https://github.com/hongxin001/logitnorm_ood
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
https://github.com/hongxin001/open-sampling
Open-set Label Noise Can Improve Robustness Against Inherent Label Noise
https://github.com/jangsoohyuk/Robust-Federated-Learning-with-Noisy-Labels
https://github.com/jyansir/text2tree
DivideMix: Learning with Noisy Labels as Semi-supervised Learning
https://github.com/kaist-dmlab/SELFIE
https://github.com/kumar10725/Keras-Determinant-based-Mutual-Information-loss-DMI-loss-
https://github.com/ljmiao/PENCIL
Probabilistic End-to-end Noise Correction for Learning with Noisy Labels
https://github.com/mhassann22/GCSAM
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/moskomule/sam.pytorch
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/ntucllab/climage_dataset
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
https://github.com/ntucllab/complementary_cifar
Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations
https://github.com/pleaseconnectwifi/PNAL
Learning with Noisy Labels for Robust Point Cloud Segmentation
https://github.com/rollovd/LookSAM
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/sayakpaul/Sharpness-Aware-Minimization-TensorFlow
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/simon20010923/DDAMFN
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/viethungluu/co-teaching
Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels
https://github.com/wangermeng2021/FastClassification
Sharpness-Aware Minimization for Efficiently Improving Generalization
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.