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Towards inheritable models for open-set domain adaptation

Kundu, JN and Venkat, N and Revanur, A and Rahul, MVR and Babu, V (2020) Towards inheritable models for open-set domain adaptation. In: 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, 14 - 19 June 2020, United States, pp. 12373-12382.

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Official URL: https://dx.doi.org/10.1109/CVPR42600.2020.01239


There has been a tremendous progress in Domain Adaptation (DA) for visual recognition tasks. Particularly, open-set DA has gained considerable attention wherein the target domain contains additional unseen categories. Existing open-set DA approaches demand access to a labeled source dataset along with unlabeled target instances. However, this reliance on co-existing source and target data is highly impractical in scenarios where data-sharing is restricted due to its proprietary nature or privacy concerns. Addressing this, we introduce a practical DA paradigm where a source-trained model is used to facilitate adaptation in the absence of the source dataset in future. To this end, we formalize knowledge inheritability as a novel concept and propose a simple yet effective solution to realize inheritable models suitable for the above practical paradigm. Further, we present an objective way to quantify inheritability to enable the selection of the most suitable source model for a given target domain, even in the absence of the source data. We provide theoretical insights followed by a thorough empirical evaluation demonstrating state-of-the-art open-set domain adaptation performance. Our code is available at https://github.com/val-iisc/inheritune. © 2020 IEEE

Item Type: Conference Paper
Publication: Proceedings of the IEEE Computer Society Conference on Computer Vision and Pattern Recognition
Publisher: IEEE Computer Society
Additional Information: cited By 0; Conference of 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020 ; Conference Date: 14 June 2020 Through 19 June 2020; Conference Code:162261
Keywords: Pattern recognition, Domain adaptation; Effective solution; Empirical evaluations; Privacy concerns; Source modeling; State of the art; Target domain; Visual recognition, Data Sharing
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 15 Mar 2021 10:26
Last Modified: 15 Mar 2021 10:26
URI: http://eprints.iisc.ac.in/id/eprint/67475

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