Target Distribution Agnostic Domain Adaptation for in-the-Wild Image Classification under Both Domain and Label Shifts

Aotian Zheng, Jenq-Neng Hwang, Rania Hussein, Farron Wallace, Kelsey Magrane, Lauren Shiosaka

Abstract: Although significant advancements have been made in Unsupervised Domain Adaptation (UDA), existing methods are typically validated on curated public datasets that do not adequately represent the complexities of real-world applications, such as imbalanced (long-tailed) label distributions and simultaneous domain and label shifts. To bridge this gap, we introduce the Target Distribution Agnostic Adaptation Network (TDAAN), a novel framework designed to facilitate robust adaptation from source to target domains under significant label shifts. Additionally, we present a new domain adaptation dataset, MS-DA, which focuses on marine species and incorporates natural domain and label shifts, a feature sorely lacking in current domain adaptation research. Our experiments show that TDAAN not only significantly outperforms the baseline UDA method but also surpasses the performance of leading UDA methods on the MS-DA dataset. Remarkably, TDAAN maintains competitive performance on standard UDA benchmarks, proving its efficacy even in scenarios with minimal label shifts. These results position TDAAN as a superior method for UDA, particularly in real-world applications characterized by complex and diverse data distributions. Our code is available at https://github.com/SEFSC/FATES-ATI-DomainAdaptationLabelShift.

External IDs:dblp:conf/icmcs/ZhengHHWMS25