Active Gradual Domain Adaptation: Dataset and Approach

Abstract

Adapting deep neural networks to the changing environments is critical in practical utility, especially for online web applications, where the data distribution changes gradually due to the evolving environments. For instance, the web photos of cellphones change gradually over years due to appearance changes. This paper deals with such a problem via active gradual domain adaptation, where the learner continually and actively selects the most informative labels from the target to enhance labeling efficiency and utilizes both labeled and unlabeled samples to improve the model adaptation under gradual domain drift. We propose the active gradual self-training (AGST) algorithm with novel designs of active pseudolabeling and gradual semi-supervised domain adaptation. Specifically, AGST pseudolabels the samples with high confidence, and selects the most informative labels from the unconfident samples based on both uncertainty and diversity, and then gradually self-trains itself by confident pseudolabels and queried labels. To study the gradual domain shift problem in the web data and verify the proposed algorithm, we create a new dataset -- Evolving-Image-Search (EVIS), collected from the web search engine and covers a 12-years range. Since the appearance of the products evolves over these years, such dataset naturally contains gradual domain drift. We extensively evaluate AGST on the synthetic dataset, real-world dataset, and EVIS dataset. AGST achieves up to 62% accuracy improvement (absolute value) against unsupervised gradual self-training with only 5% additional labels, and 19% accuracy improvement against directly applying CLUE, demonstrating the effectiveness of the designs of active pseudolabel and gradual semi-supervised domain adaptation.