Generative Data Augmentation for Few-Shot Domain Adaptation

Carlos E. López Fortín, Ikuko Nishikawa

Abstract: Domain adaptation in computer vision focuses on addressing the domain gap between source and target distributions, generally via adversarial methods or feature distribution alignment. However, most of them suppose the availability of sufficient target data to properly teach the model domain-invariant representations. Few-shot scenarios where target data is scarce pose a significant challenge for their implementation in real-world scenarios. Leveraging fine-tuned diffusion models for synthetic data augmentation, we present Generative Data Augmentation for Few-shot Domain Adaptation, a model-agnostic approach to address the Few-shot problem in domain adaptation for multi-class classification. Experimental results show that using augmented data from fine-tuned diffusion models with open-source data sets can improve average accuracy by up to 3%, as well as increase per-class accuracy between 3% to 30%, for state-of-the-art domain adaptation methods with respect to their non-augmented cou

External IDs:dblp:conf/icpram/FortinN24