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Semi Supervised Image Classification On Cifar 8

Métriques

Percentage error

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Percentage error	Paper Title	Repository
DoubleMatch	41.83± 1.22	DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision
Dash (CTA, WRN-28-8)	44.83±1.36	Dash: Semi-Supervised Learning with Dynamic Thresholding	-
DP-SSL	43.17±1.29	DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples	-
SemiReward	15.62	SemiReward: A General Reward Model for Semi-supervised Learning
NP-Match	38.67	NP-Match: When Neural Processes meet Semi-Supervised Learning
PCL (Fixmatch)	42.38±2.52	Probabilistic Contrastive Learning for Domain Adaptation
FixMatch+DM	40.25±0.95	-	-
SimMatch	37.81	SimMatch: Semi-supervised Learning with Similarity Matching
CCSSL(FixMatch)	38.81	Class-Aware Contrastive Semi-Supervised Learning
FixMatch (CTA)	49.95±3.01	FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
FlexMatch	39.94±1.62	FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
FixMatch+CR	49.23	Contrastive Regularization for Semi-Supervised Learning	-
ShrinkMatch	35.36	Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning
ReMixMatch	16.8	USB: A Unified Semi-supervised Learning Benchmark for Classification
Dash (RA, WRN-28-8)	44.76±0.96	Dash: Semi-Supervised Learning with Dynamic Thresholding	-
ReMixMatch	44.28±2.06	ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring	-
PCL (Flexmatch)	35.75±0.53	Probabilistic Contrastive Learning for Domain Adaptation
FreeMatch	37.98	FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning

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