Adversarial Discriminative Domain Adaptation

CVPR 2017 Eric TzengJudy HoffmanKate SaenkoTrevor Darrell

Adversarial learning methods are a promising approach to training robust deep networks, and can generate complex samples across diverse domains. They also can improve recognition despite the presence of domain shift or dataset bias: several adversarial approaches to unsupervised domain adaptation have recently been introduced, which reduce the difference between the training and test domain distributions and thus improve generalization performance... (read more)

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Evaluation results from the paper


Task Dataset Model Metric name Metric value Global rank Compare
Unsupervised Image-To-Image Translation SVNH-to-MNIST ADDA Classification Accuracy 76.0% # 3