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Continual Domain Adaptation through Pruning-aided Domain-specific Weight Modulation

Prasanna, B and Sanyal, S and Babu, RV (2023) Continual Domain Adaptation through Pruning-aided Domain-specific Weight Modulation. In: 2023 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops, CVPRW 2023, 18 - 22 June 2023, Vancouver, BC, Canada, pp. 2457-2463.

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Official URL: https://doi.org/10.1109/CVPRW59228.2023.00244


In this paper, we propose to develop a method to address unsupervised domain adaptation (UDA) in a practical setting of continual learning (CL). The goal is to update the model on continually changing domains while preserving domain-specific knowledge to prevent catastrophic forgetting of past-seen domains. To this end, we build a framework for preserving domain-specific features utilizing the inherent model capacity via pruning. We also perform effective inference using a novel batch-norm based metric to predict the final model parameters to be used accurately. Our approach achieves not only state-of-the-art performance but also prevents catastrophic forgetting of past domains significantly. Our code is made publicly available. 1. © 2023 IEEE.

Item Type: Conference Paper
Publication: IEEE Computer Society Conference on Computer Vision and Pattern Recognition Workshops
Publisher: IEEE Computer Society
Additional Information: The copyright for this article belongs to the Authors.
Keywords: Catastrophic forgetting; Continual learning; Domain adaptation; Domain specific; Domain-specific knowledge; Modeling parameters; State-of-the-art performance, Domain Knowledge
Department/Centre: Division of Interdisciplinary Sciences > Computational and Data Sciences
Date Deposited: 24 Nov 2023 10:32
Last Modified: 24 Nov 2023 10:32
URI: https://eprints.iisc.ac.in/id/eprint/83213

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