# Wasserstein Introspective Neural Networks

arXiv: https://arxiv.org/abs/1711.08875 · HTML: https://mlsanity.com/paper/1711.08875/

## Key facts

- Cited by 18 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Nov 2017.

## Built on by (most cited first, 18 of 18)

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- [Synthesize-It-Classifier: Learning a Generative Classifier through RecurrentSelf-analysis](https://mlsanity.com/paper/2103.14212/) (2021)
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- [Scalable Energy-Based Models via Adversarial Training: Unifying Discrimination and Generation](https://mlsanity.com/paper/2510.13872/) (2025)
- [Deep Consensus Learning](https://mlsanity.com/paper/2103.08475/) (2021)
- [EBM Life Cycle: MCMC Strategies for Synthesis, Defense, and Density Modeling](https://mlsanity.com/paper/2205.12243/) (2022)
- [STANLEY: Stochastic Gradient Anisotropic Langevin Dynamics for Learning Energy-Based Models](https://mlsanity.com/paper/2310.12667/) (2023)
- [D-CLOT: Double Closed Loop Optimal Transport for Unsupervised Action Segmentation](https://mlsanity.com/paper/2608.05877/) (2026)

## Abstract (arXiv)

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.