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Semi Supervised Image Classification On Svhn

Metrics

Accuracy

Results

Performance results of various models on this benchmark

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