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Clothing1M

아카이브 평가 51건 · 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
LRA-diffusion (CC)	Accuracy 75.7%	Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels 2023-05-31	코드 1
Super-SST (ViT-Small, 5% Labels)	Accuracy 75.7%	SST: Self-training with Self-adaptive Thresholding for Semi-supervised Learning 2025-05-31	연결 없음
SANM (DivideMix)	Accuracy 75.63%	Learning with Noisy labels via Self-supervised Adversarial Noisy Masking 2023-02-14	코드 1
CC	Accuracy 75.4%	Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels 2022-07-29	코드 1
CPC	Accuracy 75.40±0.10%	Class Prototype-based Cleaner for Label Noise Learning 2022-12-21	코드 1
Jigsaw-ViT+NCT	Accuracy 75.4%	Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer 2022-07-25	코드 1
MFRW	Accuracy 75.35%	Learning advisor networks for noisy image classification 2022-11-08	코드 1
Knockoffs-SPR	Accuracy 75.20%	Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels 2023-01-02	코드 1
PGDF	Accuracy 75.19%	Sample Prior Guided Robust Model Learning to Suppress Noisy Labels 2021-12-02	코드 1
AugDesc	Accuracy 75.11%	Augmentation Strategies for Learning with Noisy Labels 2021-03-03	코드 1
Nested+Co-teaching (ResNet-50)	Accuracy 75%	Compressing Features for Learning with Noisy Labels 2022-06-27	코드 1
SSR	Accuracy 74.91	SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise 2021-11-22	코드 1
NestedCoTeaching	Accuracy 74.9%	Boosting Co-teaching with Compression Regularization for Label Noise 2021-04-28	코드 1
ELR+	Accuracy 74.81%	Early-Learning Regularization Prevents Memorization of Noisy Labels 2020-06-30	코드 1 · 코드 2
DivideMix	Accuracy 74.76%	DivideMix: Learning with Noisy Labels as Semi-supervised Learning 2020-02-18	코드 1 · 코드 2
C2MT	Accuracy 74.61%	Cross-to-merge training with class balance strategy for learning with noisy labels 2024-04-01	코드 1
ELR+ with C2D (ResNet-50)	Accuracy 74.58 ± 0.15%	Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels 2021-03-25	코드 1
InstanceGM	Accuracy 74.40%	Instance-Dependent Noisy Label Learning via Graphical Modelling 2022-09-02	코드 1
LongReMix	Accuracy 74.38%	LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment 2021-03-06	코드 1
FINE + DivideMix	Accuracy 74.37%	FINE Samples for Learning with Noisy Labels 2021-02-23	코드 1
Negative Label Smoothing (NLS)	Accuracy 74.24%	To Smooth or Not? When Label Smoothing Meets Noisy Labels 2021-06-08	코드 1
CAL	Accuracy 74.17%	A Second-Order Approach to Learning with Instance-Dependent Label Noise 2020-12-22	코드 1
NoiseRank	Accuracy 73.82%	NoiseRank: Unsupervised Label Noise Reduction with Dependence Models 2020-03-15	연결 없음
FOCI	Accuracy 73.8%	Which Strategies Matter for Noisy Label Classification? Insight into Loss and Uncertainty 2020-08-14	연결 없음
MW-Net	Accuracy 73.72%	Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting 2019-02-20	코드 1 · 코드 2 · 코드 3
PENCIL	Accuracy 73.49%	Probabilistic End-to-end Noise Correction for Learning with Noisy Labels 2019-03-19	코드 1 · 코드 2 · 코드 3
MLNT	Accuracy 73.47%	Learning to Learn from Noisy Labeled Data 2018-12-13	코드 1
HOC	Accuracy 73.39%	Clusterability as an Alternative to Anchor Points When Learning with Noisy Labels 2021-02-10	코드 1 · 코드 2
MAE (SimCLR)	Accuracy 73.36%	Contrastive Learning Improves Model Robustness Under Label Noise 2021-04-19	코드 1
Generalized CE (SimCLR)	Accuracy 73.35%	Contrastive Learning Improves Model Robustness Under Label Noise 2021-04-19	코드 1