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Fast OT for Latent Domain Adaptation

2022-10-02 10:25:12
Siddharth Roheda, Ashkan Panahi, Hamid Krim
arXiv_CV
     
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.

Abstract (translated)

URL

https://arxiv.org/abs/2210.00479

PDF

https://arxiv.org/pdf/2210.00479.pdf

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