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Image Classification On Clothing1M

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Paper Title
LRA-diffusion (CC)75.7%Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
SANM (DivideMix)75.63%Learning with Noisy labels via Self-supervised Adversarial Noisy Masking
CC75.4%Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels
Jigsaw-ViT+NCT75.4%Jigsaw-ViT: Learning Jigsaw Puzzles in Vision Transformer
CPC75.40±0.10%Class Prototype-based Cleaner for Label Noise Learning
MFRW75.35%Learning advisor networks for noisy image classification
Knockoffs-SPR75.20%Knockoffs-SPR: Clean Sample Selection in Learning with Noisy Labels
PGDF75.19%Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
AugDesc75.11%Augmentation Strategies for Learning with Noisy Labels
Nested+Co-teaching (ResNet-50)75%Compressing Features for Learning with Noisy Labels
SSR74.91SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise
NestedCoTeaching74.9%Boosting Co-teaching with Compression Regularization for Label Noise
ELR+74.81%Early-Learning Regularization Prevents Memorization of Noisy Labels
DivideMix74.76%DivideMix: Learning with Noisy Labels as Semi-supervised Learning
C2MT74.61%Cross-to-merge training with class balance strategy for learning with noisy labels
ELR+ with C2D (ResNet-50)74.58 ± 0.15%Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
InstanceGM74.40%Instance-Dependent Noisy Label Learning via Graphical Modelling
LongReMix74.38%LongReMix: Robust Learning with High Confidence Samples in a Noisy Label Environment
FINE + DivideMix74.37%FINE Samples for Learning with Noisy Labels
Negative Label Smoothing (NLS)74.24%To Smooth or Not? When Label Smoothing Meets Noisy Labels
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