Unsupervised Domain Adaptation by Uncertain Feature Alignment

TL;DR

This paper introduces a novel unsupervised domain adaptation method that leverages model prediction uncertainty, measured via Monte-Carlo dropout, to improve feature alignment and achieve state-of-the-art results.

Contribution

It proposes UFAL, a new approach combining uncertainty-based filtering and feature alignment, which outperforms existing methods in unsupervised domain adaptation.

Findings

Surpasses recent architectures in benchmark datasets

Uses Monte-Carlo dropout for effective uncertainty estimation

Achieves state-of-the-art results in multiple challenging datasets

Abstract

Unsupervised domain adaptation (UDA) deals with the adaptation of models from a given source domain with labeled data to an unlabeled target domain. In this paper, we utilize the inherent prediction uncertainty of a model to accomplish the domain adaptation task. The uncertainty is measured by Monte-Carlo dropout and used for our proposed Uncertainty-based Filtering and Feature Alignment (UFAL) that combines an Uncertain Feature Loss (UFL) function and an Uncertainty-Based Filtering (UBF) approach for alignment of features in Euclidean space. Our method surpasses recently proposed architectures and achieves state-of-the-art results on multiple challenging datasets. Code is available on the project website.