# On the Latent Space of Wasserstein Auto-Encoders

arXiv: https://arxiv.org/abs/1802.03761 · HTML: https://mlsanity.com/paper/1802.03761/

## Key facts

- Cited by 22 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 11 Feb 2018.

## Built on by (most cited first, 22 of 22)

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- [A New Framework for Query Efficient Active Imitation Learning](https://mlsanity.com/paper/1912.13037/) (2019)
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- [A Wiener Process Perspective on Local Intrinsic Dimension Estimation Methods](https://mlsanity.com/paper/2406.17125/) (2024)
- [RENs: Relevance Encoding Networks](https://mlsanity.com/paper/2205.13061/) (2022)
- [Auto-Encoding Goodness of Fit](https://mlsanity.com/paper/2210.06546/) (2022)
- [Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networks](https://mlsanity.com/paper/2409.18374/) (2024)
- [History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutions](https://mlsanity.com/paper/2507.10201/) (2025)
- [Learning a Latent Pulse Shape Interface for Photoinjector Laser Systems](https://mlsanity.com/paper/2602.17263/) (2026)

## Abstract (arXiv)

We study the role of latent space dimensionality in Wasserstein auto-encoders (WAEs). Through experimentation on synthetic and real datasets, we argue that random encoders should be preferred over deterministic encoders. We highlight the potential of WAEs for representation learning with promising results on a benchmark disentanglement task.