Budget-Aware Pruning: Handling Multiple Domains with Less Parameters
Samuel Felipe dos Santos, Rodrigo Berriel, Thiago Oliveira-Santos,, Nicu Sebe, and Jurandy Almeida

TL;DR
This paper introduces a method for pruning multi-domain deep learning models to reduce parameters and computational costs, enabling efficient deployment on resource-limited devices while maintaining performance.
Contribution
It proposes a novel pruning technique that encourages shared filter usage across multiple domains, effectively reducing model size without sacrificing accuracy.
Findings
Achieves significant parameter reduction across multiple domains.
Maintains comparable classification performance with fewer filters.
Handles multiple domains with lower computational complexity than baseline.
Abstract
Deep learning has achieved state-of-the-art performance on several computer vision tasks and domains. Nevertheless, it still has a high computational cost and demands a significant amount of parameters. Such requirements hinder the use in resource-limited environments and demand both software and hardware optimization. Another limitation is that deep models are usually specialized into a single domain or task, requiring them to learn and store new parameters for each new one. Multi-Domain Learning (MDL) attempts to solve this problem by learning a single model capable of performing well in multiple domains. Nevertheless, the models are usually larger than the baseline for a single domain. This work tackles both of these problems: our objective is to prune models capable of handling multiple domains according to a user-defined budget, making them more computationally affordable while…
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Taxonomy
TopicsDomain Adaptation and Few-Shot Learning · Multimodal Machine Learning Applications · Machine Learning and ELM
