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 Wasserstein generative adversarial networks (WGAN) and the INN algorithm is made; (2) The explicit adoption of the WGAN term into INN results in a large enhancement to INN, achieving compelling results even with a single classifier on e.g., providing a 20 times reduction in model size over INN within texture modeling; (3) When applied to supervised classification, WINN also gives rise to greater robustness with an $88\%$ reduction of errors against adversarial examples -- improved over the result of $39\%$ by an INN-family algorithm. 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 attack.

本研究提出了Wasserstein introspective neural networks方法，它是一个生成器和一个辨别器在单个模型内。Wasserstein introspective neural networks方法通过加强introspective neural networks方法的生成建模能力而得到了显着的改进。实验结果表明，Wasserstein introspective neural networks方法在纹理、面部、和物体建模以及抵御对抗攻击等问题上具有不错的表现。

Wasserstein 内省神经网络