A Second-Order Perspective on Pruning at Initialization and Knowledge Transfer
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arXiv
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| Format: | Preprint |
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2025
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| author | Iurada, Leonardo Occhiena, Beatrice Tommasi, Tatiana |
| author_facet | Iurada, Leonardo Occhiena, Beatrice Tommasi, Tatiana |
| contents | The widespread availability of pre-trained vision models has enabled numerous deep learning applications through their transferable representations. However, their computational and storage costs often limit practical deployment. Pruning-at-Initialization has emerged as a promising approach to compress models before training, enabling efficient task-specific adaptation. While conventional wisdom suggests that effective pruning requires task-specific data, this creates a challenge when downstream tasks are unknown in advance. In this paper, we investigate how data influences the pruning of pre-trained vision models. Surprisingly, pruning on one task retains the model's zero-shot performance also on unseen tasks. Furthermore, fine-tuning these pruned models not only improves performance on original seen tasks but can recover held-out tasks' performance. We attribute this phenomenon to the favorable loss landscapes induced by extensive pre-training on large-scale datasets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_24066 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Second-Order Perspective on Pruning at Initialization and Knowledge Transfer Iurada, Leonardo Occhiena, Beatrice Tommasi, Tatiana Computer Vision and Pattern Recognition Artificial Intelligence The widespread availability of pre-trained vision models has enabled numerous deep learning applications through their transferable representations. However, their computational and storage costs often limit practical deployment. Pruning-at-Initialization has emerged as a promising approach to compress models before training, enabling efficient task-specific adaptation. While conventional wisdom suggests that effective pruning requires task-specific data, this creates a challenge when downstream tasks are unknown in advance. In this paper, we investigate how data influences the pruning of pre-trained vision models. Surprisingly, pruning on one task retains the model's zero-shot performance also on unseen tasks. Furthermore, fine-tuning these pruned models not only improves performance on original seen tasks but can recover held-out tasks' performance. We attribute this phenomenon to the favorable loss landscapes induced by extensive pre-training on large-scale datasets. |
| title | A Second-Order Perspective on Pruning at Initialization and Knowledge Transfer |
| topic | Computer Vision and Pattern Recognition Artificial Intelligence |
| url | https://arxiv.org/abs/2509.24066 |