MiddleGAN: Generate Domain Agnostic Samples for Unsupervised Domain Adaptation

TL;DR

MiddleGAN introduces a novel generative model that creates domain-agnostic samples to improve unsupervised domain adaptation, leading to better classifier performance across diverse datasets.

Contribution

The paper proposes MiddleGAN, a new GAN variation that generates samples similar to both source and target domains, enhancing domain-invariant feature learning for unsupervised domain adaptation.

Findings

MiddleGAN outperforms state-of-the-art methods by up to 20.1% on benchmarks.

Generated samples effectively resemble both source and target domain data.

Theoretical analysis confirms optimal solutions for MiddleGAN components.

Abstract

In recent years, machine learning has achieved impressive results across different application areas. However, machine learning algorithms do not necessarily perform well on a new domain with a different distribution than its training set. Domain Adaptation (DA) is used to mitigate this problem. One approach of existing DA algorithms is to find domain invariant features whose distributions in the source domain are the same as their distribution in the target domain. In this paper, we propose to let the classifier that performs the final classification task on the target domain learn implicitly the invariant features to perform classification. It is achieved via feeding the classifier during training generated fake samples that are similar to samples from both the source and target domains. We call these generated samples domain-agnostic samples. To accomplish this we propose a novel variation of generative adversarial networks (GAN), called the MiddleGAN, that generates fake samples that are similar to samples from both the source and target domains, using two discriminators and one generator. We extend the theory of GAN to show that there exist optimal solutions for the parameters of the two discriminators and one generator in MiddleGAN, and empirically show that the samples generated by the MiddleGAN are similar to both samples from the source domain and samples from the target domain. We conducted extensive evaluations using 24 benchmarks; on the 24 benchmarks, we compare MiddleGAN against various state-of-the-art algorithms and outperform the state-of-the-art by up to 20.1\% on certain benchmarks.