Image Classification On Mini Webvision 1 0

评估指标

ImageNet Top-1 Accuracy

ImageNet Top-5 Accuracy

Top-1 Accuracy

Top-5 Accuracy

评测结果

各个模型在此基准测试上的表现结果

					Paper Title	Repository
LRA-diffusion (CLIP ViT)	82.56	-	84.16	-	Label-Retrieval-Augmented Diffusion Models for Learning from Noisy Labels
Robust LR	75.48	93.76	81.84	94.12	Two Wrongs Don't Make a Right: Combating Confirmation Bias in Learning with Label Noise	-
PGDF (Inception-ResNet-v2)	75.45	93.11	81.47	94.03	Sample Prior Guided Robust Model Learning to Suppress Noisy Labels
SSR	75.76	91.76	80.92	92.80	SSR: An Efficient and Robust Framework for Learning with Unknown Label Noise
BtR	75.96	92.20	80.88	92.76	Bootstrapping the Relationship Between Images and Their Clean and Noisy Labels
CoDiM-Sup (Inception-ResNet-v2)	76.52	91.96	80.88	92.48	CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning	-
NCR+Mixup+DA (ResNet-50)	-	-	80.5	-	Learning with Neighbor Consistency for Noisy Labels
CMW-Net-SL+C2D	77.36	93.48	80.44	93.36	CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
Dynamic Loss (Inception-ResNet-v2)	74.76	93.08	80.12	93.64	Dynamic Loss For Robust Learning
CoDiM-Self (Inception-ResNet-v2)	77.24	92.48	80.12	93.52	CoDiM: Learning with Noisy Labels via Contrastive Semi-Supervised Learning	-
Sel-CL+ (ResNet-18)	76.84	93.04	79.96	92.64	Selective-Supervised Contrastive Learning with Noisy Labels
CPC	75.75±0.14	93.49±0.25	79.63±0.08	93.46±0.10	Class Prototype-based Cleaner for Label Noise Learning
PSSCL (130 epochs)	79.68	95.16	79.56	94.84	PSSCL: A progressive sample selection framework with contrastive loss designed for noisy labels	-
DivideMix with C2D (ResNet-50)	78.57 ± 0.37	93.04 ± 0.10	79.42 ± 0.34	92.32 ± 0.33	Contrast to Divide: Self-Supervised Pre-Training for Learning with Noisy Labels
FaMUS	77	92.76	79.4	92.80	Faster Meta Update Strategy for Noise-Robust Deep Learning
NCR+Mixup (ResNet-50)	-	-	79.4	-	Learning with Neighbor Consistency for Noisy Labels
CC	76.08	93.86	79.36	93.64	Centrality and Consistency: Two-Stage Clean Samples Identification for Learning with Instance-Dependent Noisy Labels
GJS (ResNet-50)	75.50	91.27	79.28	91.22	Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels
NGC (Inception-ResNet-v2)	74.44	91.04	79.16	91.84	NGC: A Unified Framework for Learning with Open-World Noisy Data	-
TCL	75.4	92.4	79.1	92.3	Twin Contrastive Learning with Noisy Labels

0 of 47 row(s) selected.

Command Palette

Image Classification On Mini Webvision 1 0

评估指标

评测结果