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Models/Triple-GAN-V2 (CNN-13)

Triple-GAN-V2 (CNN-13)

Reported on 10 benchmarks across 2 tasks · 1 paper · 2 SOTA

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

Computer Vision10 results

Image ClassificationonSVHN, 500 Labels
Accuracy· 2019-12-20
96.39
SOTA
Triple Generative Adversarial Networks arXiv:1912.09784
Semi-Supervised Image ClassificationonSVHN, 500 Labels
Accuracy· 2019-12-20
96.39
SOTA
Triple Generative Adversarial Networks arXiv:1912.09784
Image ClassificationonCIFAR-10, 4000 Labels
Percentage error· 2019-12-20
10.01
best: 3.96 (SimMatch)
Triple Generative Adversarial Networks arXiv:1912.09784
Image ClassificationonCIFAR-10, 1000 Labels
Accuracy· 2019-12-20
85
best: 92.25 (MixMatch)
Triple Generative Adversarial Networks arXiv:1912.09784
Image ClassificationonSVHN, 1000 labels
Accuracy· 2019-12-20
96.55
best: 97.58 (EnAET)
Triple Generative Adversarial Networks arXiv:1912.09784
Image ClassificationonSVHN, 250 Labels
Accuracy· 2019-12-20
96.52
best: 98.04 (ShrinkMatch)
Triple Generative Adversarial Networks arXiv:1912.09784
Semi-Supervised Image ClassificationonCIFAR-10, 4000 Labels
Percentage error· 2019-12-20
10.01
best: 3.96 (SimMatch)
Triple Generative Adversarial Networks arXiv:1912.09784
Semi-Supervised Image ClassificationonCIFAR-10, 1000 Labels
Accuracy· 2019-12-20
85
best: 92.25 (MixMatch)
Triple Generative Adversarial Networks arXiv:1912.09784
Semi-Supervised Image ClassificationonSVHN, 1000 labels
Accuracy· 2019-12-20
96.55
best: 97.58 (EnAET)
Triple Generative Adversarial Networks arXiv:1912.09784
Semi-Supervised Image ClassificationonSVHN, 250 Labels
Accuracy· 2019-12-20
96.52
best: 98.04 (ShrinkMatch)
Triple Generative Adversarial Networks arXiv:1912.09784