The Effects of Grouped Structural Global Pruning of Vision Transformers on Domain Generalisation

• URL: http://arxiv.org/abs/2504.04196v1
• Date: Sat, 05 Apr 2025 15:05:36 GMT
• Title: The Effects of Grouped Structural Global Pruning of Vision Transformers on Domain Generalisation
• Authors: Hamza Riaz, Alan F. Smeaton,
• Abstract summary: This paper introduces a novel grouped structural pruning method for pre-trained vision transformers (ViT, BeiT, and DeiT)<n>Our method uses dependency graph analysis to identify and remove redundant groups of neurons, weights, filters, or attention heads within transformers.<n>Results show significant improvements in inference speed and fine-tuning time with minimal trade-offs in accuracy and DG task performance.
• Score: 2.2124795371148616
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: With the growing sizes of AI models like large language models (LLMs) and vision transformers, deploying them on devices with limited computational resources is a significant challenge particularly when addressing domain generalisation (DG) tasks. This paper introduces a novel grouped structural pruning method for pre-trained vision transformers (ViT, BeiT, and DeiT), evaluated on the PACS and Office-Home DG benchmarks. Our method uses dependency graph analysis to identify and remove redundant groups of neurons, weights, filters, or attention heads within transformers, using a range of selection metrics. Grouped structural pruning is applied at pruning ratios of 50\%, 75\% and 95\% and the models are then fine-tuned on selected distributions from DG benchmarks to evaluate their overall performance in DG tasks. Results show significant improvements in inference speed and fine-tuning time with minimal trade-offs in accuracy and DG task performance. For instance, on the PACS benchmark, pruning ViT, BeiT, and DeiT models by 50\% using the Hessian metric resulted in accuracy drops of only -2.94\%, -1.42\%, and -1.72\%, respectively, while achieving speed boosts of 2.5x, 1.81x, and 2.15x. These findings demonstrate the effectiveness of our approach in balancing model efficiency with domain generalisation performance.

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