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Semi Supervised Image Classification On Svhn

Metrics

Accuracy

Results

Performance results of various models on this benchmark

Paper Title
Meta Pseudo Labels (WRN-28-2)98.01 ± 0.07Meta Pseudo Labels
DoubleMatch97.90 ± 0.07DoubleMatch: Improving Semi-Supervised Learning with Self-Supervision
FixMatch (CTA)97.64±0.19FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence
EnAET97.58EnAET: A Self-Trained framework for Semi-Supervised and Supervised Learning with Ensemble Transformations
UDA97.54Unsupervised Data Augmentation for Consistency Training
ReMixMatch97.17ReMixMatch: Semi-Supervised Learning with Distribution Alignment and Augmentation Anchoring
MixMatch96.73MixMatch: A Holistic Approach to Semi-Supervised Learning
Triple-GAN-V2 (CNN-13)96.55Triple Generative Adversarial Networks
ICT (WRN-28-2)96.47Interpolation Consistency Training for Semi-Supervised Learning
R2-D2 (CNN-13)96.36Repetitive Reprediction Deep Decipher for Semi-Supervised Learning
FCE96.13Flow Contrastive Estimation of Energy-Based Models
ICT96.11Interpolation Consistency Training for Semi-Supervised Learning
Mean Teacher96.05Mean teachers are better role models: Weight-averaged consistency targets improve semi-supervised deep learning results
Triple-GAN-V2 (CNN-13, no aug)96.04Triple Generative Adversarial Networks
VAT94.58Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning
SESEMI SSL (ConvNet)94.41Exploring Self-Supervised Regularization for Supervised and Semi-Supervised Learning
GAN91.89Improved Techniques for Training GANs
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Semi Supervised Image Classification On Svhn | SOTA | HyperAI