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	-

0 of 47 row(s) selected.