Fast OT for Latent Domain Adaptation
It addresses the problem of adapting models to new data distributions without labeled target data, which is incremental as it builds on existing optimal transport methods.
The paper tackles unsupervised domain adaptation by proposing an algorithm that uses optimal transport theory to learn latent feature representations, minimizing the cost of transporting target samples to the source distribution.
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.