An unsupervised adversarial domain adaptation based on variational auto-encoder

Abstract Collecting a large amount of labeled data in machine learning is always challenging. Often, even with sufficient data, domain differences can cause a shift or bias in data distribution, affecting model performance during testing. Domain adaptation methods, especially adversarial techniques, are effective solutions for these challenges. The goal is to learn a classifier for an unlabeled target dataset using a labeled source dataset, enhancing resistance to domain shifts. However, existing methods sometimes struggle with adapting the joint feature distribution across domains, resulting in negative transfer. To address this, we propose a method that forms class-specific clusters to prevent negative transfer. This method is encapsulated in an unsupervised adversarial domain adaptation framework based on a variational auto-encoder. Our structure is designed to enhance invariant and discriminative feature representation. We process source and target data through a VAE to establish a smooth latent representation. In our method, source and target data are fed into a variational auto-encoder, which produces a smooth latent representation. The feature extractor then plays an adversarial minimax game with the discriminator to learn domain-invariant features, while the feature extractor is shared between the reconstructed source and reconstructed target data. In addition, we proposed a second structure in which the domain discriminator part of the prior structure is eliminated to demonstrate the influence of the variational auto-encoder in domain adaptation. On numerous unsupervised domain adaptation benchmarks, our results indicate that our proposed model outperforms or is comparable to state-of-the-art outcomes.