Enhanced Transport Distance for Unsupervised Domain Adaptation
By Mengxue Li, Yiteng Zhai, You-Wei Luo, Pengfei Ge, Chuan-Xian Ren. 2020
201 citations recorded by the source corpus.
Unsupervised domain adaptation (UDA) is a representative problem in transfer learning, which aims to improve the classification performance on an unlabeled target domain by exploiting discriminant information from a labeled source domain. The optimal transport model has been used for UDA in the perspective of distribution matching. However, the transport distance cannot reflect the discriminant information from either domain knowledge or category prior. In this work, we propose an enhanced transport distance (ETD) for UDA. This method builds an attention-aware transport distance, which can be viewed as the prediction feedback of the iteratively learned classifier, to measure the domain discrepancy. Further, the Kantorovich potential variable is re-parameterized by deep neural networks to learn the distribution in the latent space. The entropy-based regularization is developed to explore the intrinsic structure of the target domain. The proposed method is optimized alternately in an end-to-end manner. Extensive experiments are conducted on four benchmark datasets to demonstrate the SOTA performance of ETD.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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Citations in the corpus
- Unsupervised Model Adaptation for Continual Semantic Segmentation (2020, methodology): “Wasserstein Distance (WD) has been used successfully for domain alignment in the UDA literature (Bhushan Damodaran et al. 2018b; Rostami et al. 2019; Xu et al. 2020; Li et al. 2020; Rostami and Galstyan 2020).”