Semi Supervised Image Classification On Stl 1

Metrics

Accuracy

Results

Performance results of various models on this benchmark

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

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Semi Supervised Image Classification On Stl 1 | SOTA | HyperAI