S$^3$VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation

• URL: http://arxiv.org/abs/2109.08901v1
• Date: Sat, 18 Sep 2021 10:53:57 GMT
• Title: S$^3$VAADA: Submodular Subset Selection for Virtual Adversarial Active Domain Adaptation
• Authors: Harsh Rangwani, Arihant Jain, Sumukh K Aithal and R. Venkatesh Babu
• Abstract summary: In the real-world scenario's it might be feasible to get labels for a small proportion of target data. We propose S$3$VAADA which i) introduces a novel submodular criterion to select a maximally informative subset to label and ii) enhances a cluster-based DA procedure. Our approach consistently outperforms the competing state-of-the-art approaches on datasets with varying degrees of domain shifts.
• Score: 49.01925978752677
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Unsupervised domain adaptation (DA) methods have focused on achieving maximal performance through aligning features from source and target domains without using labeled data in the target domain. Whereas, in the real-world scenario's it might be feasible to get labels for a small proportion of target data. In these scenarios, it is important to select maximally-informative samples to label and find an effective way to combine them with the existing knowledge from source data. Towards achieving this, we propose S$^3$VAADA which i) introduces a novel submodular criterion to select a maximally informative subset to label and ii) enhances a cluster-based DA procedure through novel improvements to effectively utilize all the available data for improving generalization on target. Our approach consistently outperforms the competing state-of-the-art approaches on datasets with varying degrees of domain shifts.

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