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Semi Supervised Image Classification On Svhn 1

Métriques

Accuracy

Résultats

Résultats de performance de divers modèles sur ce benchmark

Nom du modèle	Accuracy	Paper Title	Repository
VAT	91.59	Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning	-
Triple-GAN-V2 (CNN-13)	96.52	Triple Generative Adversarial Networks	-
MixMatch	96.22	MixMatch: A Holistic Approach to Semi-Supervised Learning	-
RealMix	96.47	RealMix: Towards Realistic Semi-Supervised Deep Learning Algorithms	-
Dual Student	95.76	Dual Student: Breaking the Limits of the Teacher in Semi-supervised Learning	-
MeanTeacher	93.55	Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results	-
Triple-GAN-V2 (CNN-13, no aug)	95.81	Triple Generative Adversarial Networks	-
MutexMatch (k=0.6C)	97.47	MutexMatch: Semi-Supervised Learning with Mutex-Based Consistency Regularization	-
EnAET	96.79	EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations	-
MixUp	60.03	mixup: Beyond Empirical Risk Minimization	-
Semi-MMDC	97.7±0.03	Boosting the Performance of Semi-Supervised Learning with Unsupervised Clustering	-
ShrinkMatch	98.04	Shrinking Class Space for Enhanced Certainty in Semi-Supervised Learning	-
SESEMI SSL (ConvNet)	91.68	Exploring Self-Supervised Regularization for Supervised and Semi-Supervised Learning	-
DoubleMatch	97.63±0.35	DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision	-
Ⅱ-model	82.35	Regularization With Stochastic Transformations and Perturbations for Deep Semi-Supervised Learning	-

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Semi Supervised Image Classification On Svhn 1 | SOTA | HyperAI