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전체 기법 전체 데이터셋

하위 태스크: 3
평가표: 4
보존된 평가 기록: 8

코드

관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.

https://github.com/cleanlab/cleanlab

공식 코드

Confident Learning: Estimating Uncertainty in Dataset Labels

https://github.com/deepmind/deepmind-research

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An Instance-Dependent Simulation Framework for Learning with Label Noise

https://github.com/eden500/Noisy-Labels-Instance-Segmentation

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Benchmarking Label Noise in Instance Segmentation: Spatial Noise Matters

https://github.com/erikenglesson/gjs

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Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels

https://github.com/facebookresearch/noisy_few_shot

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Few-shot Learning with Noisy Labels

https://github.com/fbladl/nvum

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NVUM: Non-Volatile Unbiased Memory for Robust Medical Image Classification

https://github.com/federatedml/fedln

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Labeling Chaos to Learning Harmony: Federated Learning with Noisy Labels

https://github.com/filipe-research/LongReMix

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LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment

https://github.com/filipe-research/anne

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ANNE: Adaptive Nearest Neighbors and Eigenvector-based Sample Selection for Robust Learning with Noisy Labels

https://github.com/filipe-research/propmix

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PropMix: Hard Sample Filtering and Proportional MixUp for Learning with Noisy Labels

https://github.com/fuxiailab/protosemi

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Rethinking Noisy Label Learning in Real-world Annotation Scenarios from the Noise-type Perspective

https://github.com/giorgiop/loss-correction

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Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach

https://github.com/google-research/sam

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Sharpness-Aware Minimization for Efficiently Improving Generalization

https://github.com/google-research/scenic

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Learning with Neighbor Consistency for Noisy Labels

https://github.com/haolsun/WarPI

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Learning to Rectify for Robust Learning with Noisy Labels

https://github.com/hcyang-null/mild

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MILD: Modeling the Instance Learning Dynamics for Learning with Noisy Labels

https://github.com/hhhhhhao/general-framework-weak-supervision

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Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations

https://github.com/hitcszx/ALFs

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Asymmetric Loss Functions for Learning with Noisy Labels

https://github.com/hitcszx/lnl_sr

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Learning with Noisy Labels via Sparse Regularization

https://github.com/hongxin001/JoCoR

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Combating noisy labels by agreement: A joint training method with co-regularization

https://github.com/hongxin001/ODNL

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Open-set Label Noise Can Improve Robustness Against Inherent Label Noise

https://github.com/hrayrhar/limit-label-memorization

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Improving Generalization by Controlling Label-Noise Information in Neural Network Weights

https://github.com/hyperconnect/fasten

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Learning with Noisy Labels by Efficient Transition Matrix Estimation to Combat Label Miscorrection

https://github.com/hzzone/tcl

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Twin Contrastive Learning with Noisy Labels

https://github.com/ican-szu/lond-drs

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Learning with Open-world Noisy Data via Class-independent Margin in Dual Representation Space

https://github.com/ilonafan/dar-noisy-labels

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Exploring Video-Based Driver Activity Recognition under Noisy Labels

https://github.com/inezyu0928/in-context-learning

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Exploring the Robustness of In-Context Learning with Noisy Labels

https://github.com/jhoon-oh/kd_data

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Comparing Kullback-Leibler Divergence and Mean Squared Error Loss in Knowledge Distillation

https://github.com/jiangwenj02/dynamic_loss

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Dynamic Loss For Robust Learning

https://github.com/jiarunliu/co-correcting

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Co-Correcting: Noise-tolerant Medical Image Classification via mutual Label Correction

https://github.com/jingcheng-du/ML_Net-1

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ML-Net: multi-label classification of biomedical texts with deep neural networks

https://github.com/jonathanwilton/RobustDecisionTrees

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Robust Loss Functions for Training Decision Trees with Noisy Labels

https://github.com/justherozen/promix

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ProMix: Combating Label Noise via Maximizing Clean Sample Utility

https://github.com/lfb-1/gnl

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Partial Label Supervision for Agnostic Generative Noisy Label Learning

https://github.com/lijichang/flnl-feddiv

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FedDiv: Collaborative Noise Filtering for Federated Learning with Noisy Labels

https://github.com/lijichang/lnl-nce

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Neighborhood Collective Estimation for Noisy Label Identification and Correction

출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.