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Poster
Cooperative Distribution Alignment via JSD Upper Bound
Wonwoong Cho · ZIYU GONG · David Inouye

Thu Dec 01 02:00 PM -- 04:00 PM (PST) @ Hall J #437

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 show empirical results on both simulated and real-world datasets to demonstrate the benefits of our approach. Code is available at https://github.com/inouye-lab/alignment-upper-bound.

Author Information

Wonwoong Cho (Purdue University)
Wonwoong Cho

I am a Ph.D. student at Purdue ECE, advised by Prof. Inouye. I have earned my M.S. degree in CSE at Korea University, advised by Prof. Jaegul Choo. My research interest lies in Generative Model and its applications.

ZIYU GONG (Purdue University)
David Inouye (Purdue University)

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