Triple Generative Adversarial Networks

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Benchmark Model Rank Results
semi-supervised-image-classification-on-cifarTriple-GAN-V2 (ResNet-26)#25Percentage error: 6.54
semi-supervised-image-classification-on-cifarTriple-GAN-V2 (CNN-13)#30Percentage error: 10.01
semi-supervised-image-classification-on-cifarTriple-GAN-V2 (CNN-13, no aug)#36Percentage error: 12.41
semi-supervised-image-classification-on-cifar-11Triple-GAN-V2 (ResNet-26)#3Accuracy: 91.59
semi-supervised-image-classification-on-cifar-11Triple-GAN-V2 (CNN-13)#5Accuracy: 85.00
semi-supervised-image-classification-on-cifar-11Triple-GAN-V2 (CNN-13, no aug)#8Accuracy: 81.81
semi-supervised-image-classification-on-svhnTriple-GAN-V2 (CNN-13)#8Accuracy: 96.55
semi-supervised-image-classification-on-svhnTriple-GAN-V2 (CNN-13, no aug)#14Accuracy: 96.04
semi-supervised-image-classification-on-svhn-1Triple-GAN-V2 (CNN-13)#6Accuracy: 96.52
semi-supervised-image-classification-on-svhn-1Triple-GAN-V2 (CNN-13, no aug)#9Accuracy: 95.81

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