Semi Supervised Image Classification On Cifar 6

Metrics

Percentage error

Results

Performance results of various models on this benchmark

Model Name	Percentage error	Paper Title	Repository
DP-SSL	4.78±0.26	DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples	-
Semi-MMDC	5.51±0.25	Boosting the Performance of Semi-Supervised Learning with Unsupervised Clustering
Ⅱ-Model	53.12	Temporal Ensembling for Semi-Supervised Learning
VAT	36.03	Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
DoubleMatch	5.56±0.42	DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision
ReMixMatch	6.27	ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring	-
SimMatch	4.84	SimMatch: Semi-supervised Learning with Similarity Matching
MixMatch	11.08	MixMatch: A Holistic Approach to Semi-Supervised Learning
FreeMatch	4.88	FreeMatch: Self-adaptive Thresholding for Semi-supervised Learning
FixMatch+CR	5.04	Contrastive Regularization for Semi-Supervised Learning	-
MixUp	47.43	mixup: Beyond Empirical Risk Minimization
FixMatch+DM	4.77±0.09	-	-
EnAET	7.6	RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms
DebiasPL (w/ FixMatch)	4.6	Debiased Learning from Naturally Imbalanced Pseudo-Labels
FixMatch (CTA)	5.07±0.33	FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
RealMix	9.79	RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms
MeanTeacher	47.32	Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
FlexMatch	4.8±0.06	FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling
Dash (RA)	4.56±0.13	Dash: Semi-Supervised Learning with Dynamic Thresholding	-
Diff-SySC	3.65±0.10	Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification	-

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