Cooperative Distribution Alignment via JSD Upper Bound

• URL: http://arxiv.org/abs/2207.02286v1
• Date: Tue, 5 Jul 2022 20:09:03 GMT
• Title: Cooperative Distribution Alignment via JSD Upper Bound
• Authors: Wonwoong Cho, Ziyu Gong, David I. Inouye
• Abstract summary: Unsupervised distribution alignment estimates a transformation that maps two or more source distributions to a shared aligned distribution. This task has many applications including generative modeling, unsupervised domain adaptation, and socially aware learning. We propose to unify and generalize previous flow-based approaches under a single non-adversarial framework.
• Score: 7.071749623370137
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Unsupervised distribution alignment estimates a transformation that maps two or more source distributions to a shared aligned distribution given only samples from each distribution. This task has many applications including generative modeling, unsupervised domain adaptation, and socially aware learning. Most prior works use adversarial learning (i.e., min-max optimization), which can be challenging to optimize and evaluate. A few recent works explore non-adversarial flow-based (i.e., invertible) approaches, but they lack a unified perspective and are limited in efficiently aligning multiple distributions. Therefore, we propose to unify and generalize previous flow-based approaches under a single non-adversarial framework, which we prove is equivalent to minimizing an upper bound on the Jensen-Shannon Divergence (JSD). Importantly, our problem reduces to a min-min, i.e., cooperative, problem and can provide a natural evaluation metric for unsupervised distribution alignment. We present empirical results of our framework on both simulated and real-world datasets to demonstrate the benefits of our approach.

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