Distilled Pruning: Using Synthetic Data to Win the Lottery

• URL: http://arxiv.org/abs/2307.03364v3
• Date: Tue, 8 Aug 2023 22:32:24 GMT
• Title: Distilled Pruning: Using Synthetic Data to Win the Lottery
• Authors: Luke McDermott, Daniel Cummings
• Abstract summary: This work introduces a novel approach to pruning deep learning models by using distilled data. Our approach can find sparse, trainableworks up to 5x faster than Iterative Magnitude Pruning at comparable sparsity on CIFAR-10. The experimental results highlight the potential of using distilled data for resource-efficient neural network pruning, model compression, and neural architecture search.
• Score: 2.4366811507669124
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: This work introduces a novel approach to pruning deep learning models by using distilled data. Unlike conventional strategies which primarily focus on architectural or algorithmic optimization, our method reconsiders the role of data in these scenarios. Distilled datasets capture essential patterns from larger datasets, and we demonstrate how to leverage this capability to enable a computationally efficient pruning process. Our approach can find sparse, trainable subnetworks (a.k.a. Lottery Tickets) up to 5x faster than Iterative Magnitude Pruning at comparable sparsity on CIFAR-10. The experimental results highlight the potential of using distilled data for resource-efficient neural network pruning, model compression, and neural architecture search.

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