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Wasserstein Auto-Encoders

arXiv 1711.01558 ↗First posted 5 Nov 2017stat.ML cs.LGcited by 371

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Abstract (arXiv)

We propose the Wasserstein Auto-Encoder (WAE)---a new algorithm for building a generative model of the data distribution. WAE minimizes a penalized form of the Wasserstein distance between the model distribution and the target distribution, which leads to a different regularizer than the one used by the Variational Auto-Encoder (VAE). This regularizer encourages the encoded training distribution to match the prior. We compare our algorithm with several other techniques and show that it is a generalization of adversarial auto-encoders (AAE). Our experiments show that WAE shares many of the properties of VAEs (stable training, encoder-decoder architecture, nice latent manifold structure) while generating samples of better quality, as measured by the FID score.

Key facts. Cited by 371 papers in the map (data through 7 Oct 2026). First posted on arXiv 5 Nov 2017.

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Citing papers per year: 2019: 2; 2020: 0; 2021: 98; 2022: 62; 2023: 78; 2024: 37; 2025: 50; 2026: 44.

371 papers.

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  2. 2021Deep Generative Modelling: A Comparative Review of VAEs, GANs, Normalizing Flows, Energy-Based and Autoregressive Modelscited by 251
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  9. 2025EQ-VAE: Equivariance Regularized Latent Space for Improved Generative Image Modelingcited by 85
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  11. 2022GlanceNets: Interpretabile, Leak-proof Concept-based Modelscited by 73
  12. 2022SQ-VAE: Variational Bayes on Discrete Representation with Self-annealed Stochastic Quantizationcited by 68
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  2. 2023Recent Advances in Optimal Transport for Machine Learningcited by 66
  3. 2021DeepSMOTE: Fusing Deep Learning and SMOTE for Imbalanced Datacited by 62
  4. 2022Verifying the Union of Manifolds Hypothesis for Image Datacited by 62
  5. 2021Consistency Regularization for Variational Auto-Encoderscited by 50
  6. 2021Point-set Distances for Learning Representations of 3D Point Cloudscited by 48
  7. 2021Detecting Model Misspecification in Amortized Bayesian Inference with Neural Networkscited by 48
  8. 2022A survey of multimodal deep generative modelscited by 47
  9. 2021Uncertainty-guided Model Generalization to Unseen Domainscited by 41
  10. 2021Improving Mini-batch Optimal Transport via Partial Transportationcited by 40
  11. 2023Reference-guided Controllable Inpainting of Neural Radiance Fieldscited by 40
  12. 2021Counterfactual State Explanations for Reinforcement Learning Agents via Generative Deep Learningcited by 38
  13. 2021DRB-GAN: A Dynamic ResBlock Generative Adversarial Network for Artistic Style Transfercited by 38
  14. 2021Automatic Text Evaluation through the Lens of Wasserstein Barycenterscited by 38
  15. 2022Mitigating Data Sparsity for Short Text Topic Modeling by Topic-Semantic Contrastive Learningcited by 38
  16. 2022Attention Consistency on Visual Corruptions for Single-Source Domain Generalizationcited by 37
  17. 2022Representing Mixtures of Word Embeddings with Mixtures of Topic Embeddingscited by 36
  18. 2021Spatio-Temporal Graph Dual-Attention Network for Multi-Agent Prediction and Trackingcited by 35
  19. 2021Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safetycited by 35
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  21. 2022Understanding Gradual Domain Adaptation: Improved Analysis, Optimal Path and Beyondcited by 32
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  24. 2022PACTran: PAC-Bayesian Metrics for Estimating the Transferability of Pretrained Models to Classification Taskscited by 30
  25. 2022LIDL: Local Intrinsic Dimension Estimation Using Approximate Likelihoodcited by 30
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  27. 2021Active Learning at the ImageNet Scalecited by 28
  28. 2022Graph Auto-Encoder Via Neighborhood Wasserstein Reconstructioncited by 28
  29. 2022Diagnosing and Fixing Manifold Overfitting in Deep Generative Modelscited by 28
  30. 2022Synthetic Data in Human Analysis: A Surveycited by 28
  31. 2022CaloMan: Fast generation of calorimeter showers with density estimation on learned manifoldscited by 28
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  33. 2021Out-of-distribution Prediction with Invariant Risk Minimization: The Limitation and An Effective Fixcited by 27
  34. 2021On Robust Optimal Transport: Computational Complexity and Barycenter Computationcited by 27
  35. 2021Outlier-Robust Optimal Transport: Duality, Structure, and Statistical Analysiscited by 27
  36. 2022Pythae: Unifying Generative Autoencoders in Python -- A Benchmarking Use Casecited by 26
  37. 2022Revisiting Sliced Wasserstein on Images: From Vectorization to Convolutioncited by 25
  38. 2022Learning Invariant Visual Representations for Compositional Zero-Shot Learningcited by 24

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