Abstract
We present Wasserstein introspective neural networks
(WINN) that are both a generator and a discriminator
within a single model. WINN provides a significant improvement over the recent introspective neural networks
(INN) method by enhancing INN’s generative modeling capability. WINN has three interesting properties: (1) A mathematical connection between the formulation of the INN algorithm and that of Wasserstein generative adversarial networks (WGAN) is made. (2) The explicit adoption of the
Wasserstein distance into INN results in a large enhancement to INN, achieving compelling results even with a single
classifier — e.g., providing nearly a 20 times reduction in
model size over INN for unsupervised generative modeling.
(3) When applied to supervised classification, WINN also
gives rise to improved robustness against adversarial examples in terms of the error reduction. In the experiments, we
report encouraging results on unsupervised learning problems including texture, face, and object modeling, as well as
a supervised classification task against adversarial attacks.
Our code is available online