Soft-Masked Semi-Dual Optimal Transport for Partial Domain Adaptation

TL;DR

This paper introduces a novel Soft-masked Semi-dual Optimal Transport method for partial domain adaptation, effectively handling domain shift and label space differences by estimating class weights, constructing soft-masked transport distances, and employing neural networks for optimization.

Contribution

The proposed SSOT method innovatively combines class-weighted reweighting, soft-masked transport, and neural network approximation to improve partial domain adaptation performance.

Findings

Outperforms existing methods on four benchmark datasets.

Effectively handles domain shift and label space mismatch.

Enhances class-conditional distribution matching.

Abstract

Visual domain adaptation aims to learn discriminative and domain-invariant representation for an unlabeled target domain by leveraging knowledge from a labeled source domain. Partial domain adaptation (PDA) is a general and practical scenario in which the target label space is a subset of the source one. The challenges of PDA exist due to not only domain shift but also the non-identical label spaces of domains. In this paper, a Soft-masked Semi-dual Optimal Transport (SSOT) method is proposed to deal with the PDA problem. Specifically, the class weights of domains are estimated, and then a reweighed source domain is constructed, which is favorable in conducting class-conditional distribution matching with the target domain. A soft-masked transport distance matrix is constructed by category predictions, which will enhance the class-oriented representation ability of optimal transport in the shared feature space. To deal with large-scale optimal transport problems, the semi-dual formulation of the entropy-regularized Kantorovich problem is employed since it can be optimized by gradient-based algorithms. Further, a neural network is exploited to approximate the Kantorovich potential due to its strong fitting ability. This network parametrization also allows the generalization of the dual variable outside the supports of the input distribution. The SSOT model is built upon neural networks, which can be optimized alternately in an end-to-end manner. Extensive experiments are conducted on four benchmark datasets to demonstrate the effectiveness of SSOT.