https://github.com/MindCode-4/code-13/tree/main/Scalable-Sharpness-Aware-Minimization
Sharpness-Aware Minimization for Efficiently Improving Generalization
Papers with Code 아카이브
Document Text Classification
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관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/MindSpore-scientific-2/code-3/tree/main/Three-Dimensional-Lip-Motion-Network-for-Text-Independent-Speaker-Recognition-master
https://github.com/MohammadForouhesh/Computational-Political-Science-Papers-Implementations
https://github.com/NiMlr/pynlqn
Sharpness-Aware Minimization for Efficiently Improving Generalization
https://github.com/RituYadav92/Lightweighted-CNN-for-Document-Classification
https://github.com/Sunarker/Safeguarded-Dynamic-Label-Regression-for-Noisy-Supervision
Safeguarded Dynamic Label Regression for Generalized Noisy Supervision
https://github.com/XinshaoAmosWang/DerivativeManipulation
Normalized Loss Functions for Deep Learning with Noisy Labels
https://github.com/XinshaoAmosWang/Emphasis-Regularisation-by-Gradient-Rescaling
Normalized Loss Functions for Deep Learning with Noisy Labels
https://github.com/XinshaoAmosWang/Improving-Mean-Absolute-Error-against-CCE
Normalized Loss Functions for Deep Learning with Noisy Labels
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/artelab/Image-and-Text-fusion-for-UPMC-Food-101-using-BERT-and-CNNs
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
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.