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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/1998v7/item

공식 코드

Debiased Sample Selection for Combating Noisy Labels

https://github.com/1998v7/self-filtering

공식 코드

Self-Filtering: A Noise-Aware Sample Selection for Label Noise with Confidence Penalization

https://github.com/3778/icd-prediction-mimic

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Predicting Multiple ICD-10 Codes from Brazilian-Portuguese Clinical Notes

https://github.com/BaeHeeSun/NPC

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From Noisy Prediction to True Label: Noisy Prediction Calibration via Generative Model

https://github.com/BaeHeeSun/RENT

공식 코드

Dirichlet-based Per-Sample Weighting by Transition Matrix for Noisy Label Learning

https://github.com/Bidur-Khanal/imbalanced-medical-active-label-cleaning

공식 코드

Active Label Refinement for Robust Training of Imbalanced Medical Image Classification Tasks in the Presence of High Label Noise

https://github.com/ContrastToDivide/C2D

공식 코드

Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels

https://github.com/HanxunH/Active-Passive-Losses

공식 코드

Normalized Loss Functions for Deep Learning with Noisy Labels

https://github.com/Hua-YS/Multi-Scene-Recognition

공식 코드

MultiScene: A Large-scale Dataset and Benchmark for Multi-scene Recognition in Single Aerial Images

https://github.com/KentoNishi/Augmentation-for-LNL

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Augmentation Strategies for Learning with Noisy Labels

https://github.com/KevinCarpricorn/EchoAlign

공식 코드

Can We Treat Noisy Labels as Accurate?

https://github.com/Kthyeon/FINE_official

공식 코드

FINE Samples for Learning with Noisy Labels

https://github.com/LiJunnan1992/DivideMix

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DivideMix: Learning with Noisy Labels as Semi-supervised Learning

https://github.com/LiJunnan1992/MLNT

공식 코드

Learning to Learn from Noisy Labeled Data

https://github.com/MitchellKT/Enhanced-Meta-Label-Correction

공식 코드

Enhanced Meta Label Correction for Coping with Label Corruption

https://github.com/MrChenFeng/SSR_BMVC2022

공식 코드

SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise

https://github.com/Newbeeer/L_DMI

공식 코드

L_DMI: An Information-theoretic Noise-robust Loss Function

https://github.com/UCSC-REAL/HOC

공식 코드

Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels

https://github.com/UCSC-REAL/cores

공식 코드

Learning with Instance-Dependent Label Noise: A Sample Sieve Approach

https://github.com/YisenWang/symmetric_cross_entropy_for_noisy_labels

공식 코드

Symmetric Cross Entropy for Robust Learning with Noisy Labels

https://github.com/YutingLi0606/SURE

공식 코드

SURE: SUrvey REcipes for building reliable and robust deep networks

https://github.com/ZhaoweiZhu1995/HOC

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Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels

https://github.com/aimagelab/mammoth

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May the Forgetting Be with You: Alternate Replay for Learning with Noisy Labels

https://github.com/allegro/allenoise

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AlleNoise: large-scale text classification benchmark dataset with real-world label noise

https://github.com/anasedova/agra

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Learning with Noisy Labels by Adaptive Gradient-Based Outlier Removal

https://github.com/ashfarhangi/Protoformer

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Protoformer: Embedding Prototypes for Transformers

https://github.com/bbrattoli/JigsawPuzzlePytorch

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Improving Medical Image Classification in Noisy Labels Using Only Self-supervised Pretraining

https://github.com/bhanML/Co-teaching

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Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels

https://github.com/btsmart/bootstrapping-label-noise

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Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels

https://github.com/bupt-ai-cz/HSA-NRL

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Hard Sample Aware Noise Robust Learning for Histopathology Image Classification

https://github.com/bupt-ai-cz/PGDF

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Sample Prior Guided Robust Model Learning to Suppress Noisy Labels

https://github.com/changwxx/csot-for-lnl

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CSOT: Curriculum and Structure-Aware Optimal Transport for Learning with Noisy Labels

https://github.com/chenchenzong/dpc

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Dirichlet-Based Prediction Calibration for Learning with Noisy Labels

https://github.com/chengtan9907/co-training-based_noisy-label-learning

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Co-learning: Learning from Noisy Labels with Self-supervision

https://github.com/chenpf1025/RobustnessAccuracy

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Robustness of Accuracy Metric and its Inspirations in Learning with Noisy Labels

https://github.com/cityu-aim-group/jcas

공식 코드

Joint Class-Affinity Loss Correction for Robust Medical Image Segmentation with Noisy Labels

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