LAUSR

Domain adaptation is a promising way to ease the costly data labeling process in the era of deep learning (DL). A practical situation is partial domain adaptation (PDA), where the label space of the target domain is a subset of that in the source domain. Although existing methods yield appealing performance in PDA tasks, it is highly presumable that computation overhead exists in deep PDA models since the target is only a subtask of the original problem. In this work, PDA and model compression are seamlessly integrated into a unified training process. The cross-domain distribution divergence is reduced by minimizing a soft-weighted maximum mean discrepancy (SWMMD), which is differentiable and functions as regularization during network training. We use gradient statistics to compress the overparameterized model to identify and prune redundant channels based on the corresponding scaling factors in batch normalization (BN) layers. The experimental results demonstrate that our method can achieve comparable classification performance to state-of-the-art methods on various PDA tasks, with a significant reduction in model size and computation overhead.