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CIFAR-10N-Aggregate

아카이브 평가 26건 · 마지막 적재/변경 2026-10-30

평가 기록

과거 수집 기록이며 최신 순위가 아닙니다. 데이터셋 버전·split·실험 조건과 지표 방향이 검증되지 않은 결과는 직접 비교할 수 없습니다. 날짜는 논문 날짜이며 최신 평가일을 뜻하지 않습니다.

표가 화면보다 넓으면 좌우로 스크롤하여 논문과 구현 링크를 확인하세요.

원본 기록 순서 · 순위 미부여
모델	원본 지표·값	논문	구현
ProMix	Accuracy (mean) 97.39	ProMix: Combating Label Noise via Maximizing Clean Sample Utility 2022-07-21	코드 1
PSSCL	Accuracy (mean) 96.41	PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels 2024-12-18	코드 1
PGDF	Accuracy (mean) 96.11	Sample Prior Guided Robust Model Learning to Suppress Noisy Labels 2021-12-02	코드 1
SOP+	Accuracy (mean) 95.61	Robust Training under Label Noise by Over-parameterization 2022-02-28	코드 1
ILL	Accuracy (mean) 95.47	Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations 2023-05-22	코드 1
CORES*	Accuracy (mean) 95.25	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach 2020-10-05	코드 1
Divide-Mix	Accuracy (mean) 95.01	DivideMix: Learning with Noisy Labels as Semi-supervised Learning 2020-02-18	코드 1 · 코드 2
ELR+	Accuracy (mean) 94.83	Early-Learning Regularization Prevents Memorization of Noisy Labels 2020-06-30	코드 1 · 코드 2
PES (Semi)	Accuracy (mean) 94.66	Understanding and Improving Early Stopping for Learning with Noisy Labels 2021-06-30	코드 1
GNL	Accuracy (mean) 92.57	Partial Label Supervision for Agnostic Generative Noisy Label Learning 2023-08-02	코드 1
ELR	Accuracy (mean) 92.38	Early-Learning Regularization Prevents Memorization of Noisy Labels 2020-06-30	코드 1 · 코드 2
CAL	Accuracy (mean) 91.97	Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels 2021-02-10	코드 1 · 코드 2
Negative-LS	Accuracy (mean) 91.97	To Smooth or Not? When Label Smoothing Meets Noisy Labels 2021-06-08	코드 1
F-div	Accuracy (mean) 91.64	When Optimizing $f$-divergence is Robust with Label Noise 2020-11-07	코드 1 · 코드 2
Positive-LS	Accuracy (mean) 91.57	Does label smoothing mitigate label noise? 2020-03-05	연결 없음
JoCoR	Accuracy (mean) 91.44	Combating noisy labels by agreement: A joint training method with co-regularization 2020-03-05	코드 1 · 코드 2
CORES	Accuracy (mean) 91.23	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach 2020-10-05	코드 1
Co-Teaching	Accuracy (mean) 91.20	Co-teaching: Robust Training of Deep Neural Networks with Extremely Noisy Labels 2018-04-18	코드 1 · 코드 2 · 코드 3 · 코드 4 · 코드 5
Peer Loss	Accuracy (mean) 90.75	Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates 2019-10-08	코드 1 · 코드 2
Co-Teaching+	Accuracy (mean) 90.61	How does Disagreement Help Generalization against Label Corruption? 2019-01-14	코드 1 · 코드 2 · 코드 3
VolMinNet	Accuracy (mean) 89.70	Provably End-to-end Label-Noise Learning without Anchor Points 2021-02-04	코드 1
T-Revision	Accuracy (mean) 88.52	Are Anchor Points Really Indispensable in Label-Noise Learning? 2019-06-01	코드 1
Forward-T	Accuracy (mean) 88.24	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach 2016-09-13	코드 1 · 코드 2
Backward-T	Accuracy (mean) 88.13	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach 2016-09-13	코드 1 · 코드 2
GCE	Accuracy (mean) 87.85	Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels 2018-05-20	코드 1 · 코드 2 · 코드 3 · 코드 4
CE	Accuracy (mean) 87.77	연결 없음 논문 날짜 미기재	연결 없음