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Models/Triple-GAN-V2 (CNN-13, no aug)

Triple-GAN-V2 (CNN-13, no aug)

Reported on 10 benchmarks across 2 tasks · 1 paper

Note: results are matched by exact model name. Different papers may use the same name for different model variants.

Computer Vision10 results

Image ClassificationonCIFAR-10, 4000 Labels
Percentage error· 2019-12-20
12.41
best: 3.96 (SimMatch)
Triple Generative Adversarial Networks arXiv:1912.09784
Image ClassificationonSVHN, 500 Labels
Accuracy· 2019-12-20
96.16
best: 96.39 (Triple-GAN-V2 (CNN-13))
Triple Generative Adversarial Networks arXiv:1912.09784
Image ClassificationonCIFAR-10, 1000 Labels
Accuracy· 2019-12-20
81.81
best: 92.25 (MixMatch)
Triple Generative Adversarial Networks arXiv:1912.09784
Image ClassificationonSVHN, 1000 labels
Accuracy· 2019-12-20
96.04
best: 97.58 (EnAET)
Triple Generative Adversarial Networks arXiv:1912.09784
Image ClassificationonSVHN, 250 Labels
Accuracy· 2019-12-20
95.81
best: 98.04 (ShrinkMatch)
Triple Generative Adversarial Networks arXiv:1912.09784
Semi-Supervised Image ClassificationonCIFAR-10, 4000 Labels
Percentage error· 2019-12-20
12.41
best: 3.96 (SimMatch)
Triple Generative Adversarial Networks arXiv:1912.09784
Semi-Supervised Image ClassificationonSVHN, 500 Labels
Accuracy· 2019-12-20
96.16
best: 96.39 (Triple-GAN-V2 (CNN-13))
Triple Generative Adversarial Networks arXiv:1912.09784
Semi-Supervised Image ClassificationonCIFAR-10, 1000 Labels
Accuracy· 2019-12-20
81.81
best: 92.25 (MixMatch)
Triple Generative Adversarial Networks arXiv:1912.09784
Semi-Supervised Image ClassificationonSVHN, 1000 labels
Accuracy· 2019-12-20
96.04
best: 97.58 (EnAET)
Triple Generative Adversarial Networks arXiv:1912.09784
Semi-Supervised Image ClassificationonSVHN, 250 Labels
Accuracy· 2019-12-20
95.81
best: 98.04 (ShrinkMatch)
Triple Generative Adversarial Networks arXiv:1912.09784