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CIFAR-10N-Random3

아카이브 평가 23건 · 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
PSSCL	Accuracy (mean) 96.49	PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels 2024-12-18	코드 1
SOP+	Accuracy (mean) 95.39	Robust Training under Label Noise by Over-parameterization 2022-02-28	코드 1
ILL	Accuracy (mean) 95.13	Imprecise Label Learning: A Unified Framework for Learning with Various Imprecise Label Configurations 2023-05-22	코드 1
CORES*	Accuracy (mean) 94.74	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach 2020-10-05	코드 1
ELR+	Accuracy (mean) 94.34	Early-Learning Regularization Prevents Memorization of Noisy Labels 2020-06-30	코드 1 · 코드 2
GNL	Accuracy (mean) 91.83	Partial Label Supervision for Agnostic Generative Noisy Label Learning 2023-08-02	코드 1
ELR	Accuracy (mean) 91.41	Early-Learning Regularization Prevents Memorization of Noisy Labels 2020-06-30	코드 1 · 코드 2
CAL	Accuracy (mean) 90.74	Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels 2021-02-10	코드 1 · 코드 2
Co-Teaching	Accuracy (mean) 90.15	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
Negative-LS	Accuracy (mean) 90.13	To Smooth or Not? When Label Smoothing Meets Noisy Labels 2021-06-08	코드 1
JoCoR	Accuracy (mean) 90.11	Combating noisy labels by agreement: A joint training method with co-regularization 2020-03-05	코드 1 · 코드 2
Divide-Mix	Accuracy (mean) 89.97	DivideMix: Learning with Noisy Labels as Semi-supervised Learning 2020-02-18	코드 1 · 코드 2
Positive-LS	Accuracy (mean) 89.82	Does label smoothing mitigate label noise? 2020-03-05	연결 없음
CORES	Accuracy (mean) 89.79	Learning with Instance-Dependent Label Noise: A Sample Sieve Approach 2020-10-05	코드 1
F-div	Accuracy (mean) 89.55	When Optimizing $f$-divergence is Robust with Label Noise 2020-11-07	코드 1 · 코드 2
Co-Teaching+	Accuracy (mean) 89.54	How does Disagreement Help Generalization against Label Corruption? 2019-01-14	코드 1 · 코드 2 · 코드 3
Peer Loss	Accuracy (mean) 88.57	Peer Loss Functions: Learning from Noisy Labels without Knowing Noise Rates 2019-10-08	코드 1 · 코드 2
VolMinNet	Accuracy (mean) 88.19	Provably End-to-end Label-Noise Learning without Anchor Points 2021-02-04	코드 1
T-Revision	Accuracy (mean) 87.79	Are Anchor Points Really Indispensable in Label-Noise Learning? 2019-06-01	코드 1
GCE	Accuracy (mean) 87.58	Generalized Cross Entropy Loss for Training Deep Neural Networks with Noisy Labels 2018-05-20	코드 1 · 코드 2 · 코드 3 · 코드 4
Forward-T	Accuracy (mean) 87.04	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach 2016-09-13	코드 1 · 코드 2
Backward-T	Accuracy (mean) 86.86	Making Deep Neural Networks Robust to Label Noise: a Loss Correction Approach 2016-09-13	코드 1 · 코드 2
CE	Accuracy (mean) 85.16	연결 없음 논문 날짜 미기재	연결 없음