Unsupervised domain adaptation is a challenging task that aims to estimate a
transferable model for unlabeled target domain by exploiting source labeled
data. Optimal Transport (OT) based methods recently have been proven to be a
promising direction for domain adaptation due to their competitive performance.
However, most of these methods coarsely aligned source and target
distributions, leading to the over-aligned problem where the
category-discriminative information is mixed up although domain-invariant
representations can be learned. In this paper, we propose a Deep Hierarchical
Optimal Transport method (DeepHOT) for unsupervised domain adaptation. The main
idea is to use hierarchical optimal transport to learn both domain-invariant
and category-discriminative representations by mining the rich structural
correlations among domain data. The DeepHOT framework consists of a
domain-level OT and an image-level OT, where the latter is used as the ground
distance metric for the former. The image-level OT captures structural
associations of local image regions that are beneficial to image
classification, while the domain-level OT learns domain-invariant
representations by leveraging the underlying geometry of domains. However, due
to the high computational complexity, the optimal transport based models are
limited in some scenarios. To this end, we propose a robust and efficient
implementation of the DeepHOT framework by approximating origin OT with sliced
Wasserstein distance in image-level OT and using a mini-batch unbalanced
optimal transport for domain-level OT. Extensive experiments show that DeepHOT
surpasses the state-of-the-art methods in four benchmark datasets. Code will be
released on GitHub.Comment: 9 pages, 3 figure