Sliced-Wasserstein normalizing flows: beyond maximum likelihood training

• URL: http://arxiv.org/abs/2207.05468v1
• Date: Tue, 12 Jul 2022 11:29:49 GMT
• Title: Sliced-Wasserstein normalizing flows: beyond maximum likelihood training
• Authors: Florentin Coeurdoux and Nicolas Dobigeon and Pierre Chainais
• Abstract summary: normalizing flows generally suffer from several shortcomings including their tendency to generate unrealistic data. This paper proposes a new training paradigm based on a hybrid objective function combining the maximum likelihood principle (MLE) and a sliced-Wasserstein distance.
• Score: 12.91637880428221
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Despite their advantages, normalizing flows generally suffer from several shortcomings including their tendency to generate unrealistic data (e.g., images) and their failing to detect out-of-distribution data. One reason for these deficiencies lies in the training strategy which traditionally exploits a maximum likelihood principle only. This paper proposes a new training paradigm based on a hybrid objective function combining the maximum likelihood principle (MLE) and a sliced-Wasserstein distance. Results obtained on synthetic toy examples and real image data sets show better generative abilities in terms of both likelihood and visual aspects of the generated samples. Reciprocally, the proposed approach leads to a lower likelihood of out-of-distribution data, demonstrating a greater data fidelity of the resulting flows.

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