Semi Supervised Image Classification On Cifar

Métriques

Percentage error

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Percentage error	Paper Title	Repository
GAN	15.59	Improved Techniques for Training GANs	-
SimMatch	3.96	SimMatch: Semi-supervised Learning with Similarity Matching	-
Self Meta Pseudo Labels	4.09	Self Meta Pseudo Labels: Meta Pseudo Labels Without The Teacher	-
FixMatch+DM	4.13±0.11	-	-
LiDAM	7.48	LiDAM: Semi-Supervised Learning with Localized Domain Adaptation and Iterative Matching	-
Meta Pseudo Labels (WRN-28-2)	3.89± 0.07	Meta Pseudo Labels	-
LaplaceNet (CNN-13)	4.99±0.08	LaplaceNet: A Hybrid Graph-Energy Neural Network for Deep Semi-Supervised Classification	-
ReMixMatch	5.14	ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring	-
EnAET	4.18	EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations	-
DoubleMatch	4.65±0.17	DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision	-
UDA	5.27	Unsupervised Data Augmentation for Consistency Training	-
VAT	11.36	Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning	-
Triple-GAN-V2 (CNN-13)	10.01	Triple Generative Adversarial Networks	-
GLOT-DR	10.6	Global-Local Regularization Via Distributional Robustness	-
FlexMatch	4.19±0.01	FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling	-
UPS (Shake-Shake)	4.86	In Defense of Pseudo-Labeling: An Uncertainty-Aware Pseudo-label Selection Framework for Semi-Supervised Learning	-
SWSA	5	There Are Many Consistent Explanations of Unlabeled Data: Why You Should Average	-
Diff-SySC	3.26±0.06	Diff-SySC: An Approach Using Diffusion Models for Semi-Supervised Image Classification	-
Dual Student (600)	8.89	Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning	-
Dash (RA, ours)	4.08±0.06	Dash: Semi-Supervised Learning with Dynamic Thresholding	-

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