Reliable Weighted Optimal Transport for Unsupervised Domain Adaptation

By Renjun Xu, Pelen Liu, Liyan Wang, Chao Chen, Jindong Wang, Kaiming He, X. Zhang, Shaoqing Ren, Mingsheng Long, Zhangjie Cao and 1 more. 2020

161 citations recorded by the source corpus.

Recently, extensive researches have been proposed to address the UDA problem, which aims to learn transferrable models for the unlabeled target domain. Among them, the optimal transport is a promising metric to align the representations of the source and target domains. However, most existing works based on optimal transport ignore the intra-domain structure, only achieving coarse pair-wise matching. The target samples distributed near the edge of the clusters, or far from their corresponding class centers are easily to be misclassified by the decision boundary learned from the source domain. In this paper, we present Reliable Weighted Optimal Transport (RWOT) for unsupervised domain adaptation, including novel Shrinking Subspace Reliability (SSR) and weighted optimal transport strategy. Specifically, SSR exploits spatial prototypical information and intra-domain structure to dynamically measure the sample-level domain discrepancy across domains. Besides, the weighted optimal transport strategy based on SSR is exploited to achieve the precise-pair-wise optimal transport procedure, which reduces negative transfer brought by the samples near decision boundaries in the target domain. RWOT also equips with the discriminative centroid clustering exploitation strategy to learn transfer features. A thorough evaluation shows that RWOT outperforms existing state-of-the-art method on standard domain adaptation benchmarks.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yunchao Wei
2. Yi Yang
3. Guoliang Kang
4. Serban Stan
5. Mohammad Rostami
6. Yi Zhu
7. Guangrui Li
8. Trevor Darrell

In Computer Vision and Pattern Recognition

1. Yunchao Wei
2. Yi Yang
3. Mohammad Rostami
4. Guoliang Kang
5. Luc Van Gool
6. Dacheng Tao
7. Jiashi Feng
8. Trevor Darrell

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).”
• Domain Consensus Clustering for Universal Domain Adaptation (2021, methodology): “Some methods minimize the discrepancy in the feature space directly [40, 39, 56, 59, 42, 61, 30, 16].”