Fast OT for Latent Domain Adaptation

TL;DR

This paper introduces a fast optimal transport-based algorithm for unsupervised latent domain adaptation, effectively aligning target and source data distributions to improve model transferability.

Contribution

It presents a novel, efficient optimal transport method for learning latent feature representations in unsupervised domain adaptation tasks.

Findings

Efficient alignment of target and source distributions.

Improved adaptation performance in unsupervised settings.

Scalable solution for latent domain adaptation.

Abstract

In this paper, we address the problem of unsupervised Domain Adaptation. The need for such an adaptation arises when the distribution of the target data differs from that which is used to develop the model and the ground truth information of the target data is unknown. We propose an algorithm that uses optimal transport theory with a verifiably efficient and implementable solution to learn the best latent feature representation. This is achieved by minimizing the cost of transporting the samples from the target domain to the distribution of the source domain.