Abstract
Fine-grained domain adaptation is an emerging yet very challenging task in representation learning. In this paper, we analyze a possible reason for the poor performance in fine-grained domain adaptation, which is the difficulty in striking a balance between distribution alignment and fine-grained variations elimination. Furthermore, we propose an adversarial fine-grained domain adaptation framework as a step towards alleviating the underlying conflict between fine-grained variations elimination and domain adaptation. Specifically, our adversarial framework consists of two key modules: a joint label predictor for conditional distribution alignment and a rectifier for fine-grained variations elimination. The key balance can be achieved through the adversarial learning. Besides, experiments on domain adaptation benchmark and fine-grained dataset validate the effectiveness of our framework and show that our framework consistently outperforms the state-of-the-art methods including RTN, MADA, Multi-Task, and DASA.
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Yu, H., Jiang, R., Li, A. (2020). Striking a Balance in Unsupervised Fine-Grained Domain Adaptation Using Adversarial Learning. In: Li, G., Shen, H., Yuan, Y., Wang, X., Liu, H., Zhao, X. (eds) Knowledge Science, Engineering and Management. KSEM 2020. Lecture Notes in Computer Science(), vol 12275. Springer, Cham. https://doi.org/10.1007/978-3-030-55393-7_36
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