The paper challenges traditional assumptions about network pruning by focusing on structured pruning methods, which remove entire groups of weights, and their impact on efficiency and performance in deep learning models. The research explores the effectiveness of training pruned models from scratch compared to fine-tuning, highlighting the significance of architecture search in network pruning.

Key takeaways for engineers and specialists include the importance of shifting focus from weight selection to architecture search in network pruning. Training pruned models from scratch can often yield comparable or better results than fine-tuning, particularly for structured pruning methods. Automatic pruning methods offer an efficient way to identify more parameter-efficient network structures, potentially leading to the development of more scalable and powerful deep learning models.

Read full paper: https://arxiv.org/abs/1810.05270

Tags: Deep Learning, Optimization, Systems and Performance

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