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Wasserstein Auto-Encoders
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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.
Forward citations
Cited by 14 Pith papers
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$\mathbf{\lambda}$-VAE: Variance Equalization for Posterior Collapse
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Convex relaxation approaches for high-dimensional optimal transport
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Wasserstein normalized autoencoder for anomaly detection
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Imaging 3D polarization dynamics via deep learning 4D-STEM
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Variational Sparse Paired Autoencoders (vsPAIR) for Inverse Problems and Uncertainty Quantification
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Generalization in VAE and Diffusion Models: A Unified Information-Theoretic Analysis
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From Points to Spheres: A Geometric Reinterpretation of Variational Autoencoders
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Joint-stochastic-approximation Autoencoders with Application to Semi-supervised Learning
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