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arXiv · 2304.07560

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

Prasanna B·Sunandini Sanyal·R. Venkatesh Babu
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Abstract

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.

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Prasanna B, Sunandini Sanyal, R. Venkatesh Babu. 2023-04-15. Continual Domain Adaptation through Pruning-aided Domain-specific Weight Modulation. https://arxiv.org/abs/2304.07560

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