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On the Latent Space of Wasserstein Auto-Encoders

arXiv 1802.03761 ↗First posted 11 Feb 2018stat.ML cs.LGcited by 22

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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.

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

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Citing papers per year: 2019: 1; 2020: 0; 2021: 4; 2022: 7; 2023: 5; 2024: 3; 2025: 1; 2026: 1.

22 papers.

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  1. 2021Hyperbolic Deep Neural Networks: A Surveycited by 139
  2. 2022DIME: Fine-grained Interpretations of Multimodal Models via Disentangled Local Explanationscited by 34
  3. 2022SceneTrilogy: On Human Scene-Sketch and its Complementarity with Photo and Textcited by 30
  4. 2022LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihoodcited by 30
  5. 2021Disentangled Recurrent Wasserstein Autoencodercited by 22
  6. 2022A survey of unsupervised learning methods for high-dimensional uncertainty quantification in black-box-type problemscited by 20
  7. 2023Unbalanced Optimal Transport: A Unified Framework for Object Detectioncited by 15
  8. 2021Cauchy-Schwarz Regularized Autoencodercited by 13
  9. 2022Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style Transfercited by 7
  10. 2023Learning Disentangled Semantic Spaces of Explanations via Invertible Neural Networkscited by 6
  11. 2023Graph-Induced Syntactic-Semantic Spaces in Transformer-Based Variational AutoEncoderscited by 6
  12. 2024Improving Semantic Control in Discrete Latent Spaces with Transformer Quantized Variational Autoencoderscited by 6
10 more
  1. 2021Spread Flows for Manifold Modellingcited by 4
  2. 2023LlaMaVAE: Guiding Large Language Model Generation via Continuous Latent Sentence Spacescited by 3
  3. 2019A New Framework for Query Efficient Active Imitation Learningcited by 2
  4. 2023Prior Based Online Lane Graph Extraction from Single Onboard Camera Imagecited by 2
  5. 2024A Wiener Process Perspective on Local Intrinsic Dimension Estimation Methodscited by 1
  6. 2022RENs: Relevance Encoding Networkscited by 0
  7. 2022Auto-Encoding Goodness of Fitcited by 0
  8. 2024Adaptive Learning of the Latent Space of Wasserstein Generative Adversarial Networkscited by 0
  9. 2025History Matching under Uncertainty of Geological Scenarios with Implicit Geological Realism Control with Generative Deep Learning and Graph Convolutionscited by 0
  10. 2026Learning a Latent Pulse Shape Interface for Photoinjector Laser Systemscited by 0