AVATAR: Adversarial self-superVised domain Adaptation network for TARget domain
This addresses domain adaptation for machine learning applications where labeled data is scarce, but it appears incremental as it builds on existing UDA techniques.
The paper tackles the problem of unsupervised domain adaptation (UDA) with significant domain gaps by proposing the AVATAR algorithm, which outperforms state-of-the-art methods on three UDA benchmarks through domain adversarial learning, self-supervised learning, and sample selection.
This paper presents an unsupervised domain adaptation (UDA) method for predicting unlabeled target domain data, specific to complex UDA tasks where the domain gap is significant. Mainstream UDA models aim to learn from both domains and improve target discrimination by utilizing labeled source domain data. However, the performance boost may be limited when the discrepancy between the source and target domains is large or the target domain contains outliers. To explicitly address this issue, we propose the Adversarial self-superVised domain Adaptation network for the TARget domain (AVATAR) algorithm. It outperforms state-of-the-art UDA models by concurrently reducing domain discrepancy while enhancing discrimination through domain adversarial learning, self-supervised learning, and sample selection strategy for the target domain, all guided by deep clustering. Our proposed model significantly outperforms state-of-the-art methods on three UDA benchmarks, and extensive ablation studies and experiments demonstrate the effectiveness of our approach for addressing complex UDA tasks.