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Semi Supervised Image Classification On Stl 1

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
ReMixMatch	93.82	ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring	-
MixMatch	89.82	MixMatch: A Holistic Approach to Semi-Supervised Learning	-
SWWAE	74.30	Stacked What-Where Auto-encoders	-
SimCLR (CoMatch)	77.46	CoMatch: Semi-supervised Learning with Contrastive Graph Regularization	-
Diff-SySC	99.36±0.20	Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification	-
DoubleMatch	95.65±0.20	DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision	-
EnAET	91.96	EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations	-
FixMatch (CTA)	94.83±0.63	FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence	-
Semi-MMDC	95.22±0.29	Boosting the Performance of Semi-Supervised Learning with Unsupervised Clustering	-
CC-GAN²	77.80	Semi-Supervised Learning with Context-Conditional Generative Adversarial Networks	-
NP-Match	94.53	NP-Match: When Neural Processes meet Semi-Supervised Learning	-

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Semi Supervised Image Classification On Stl 1 | SOTA | HyperAI