Convex Distillation: Efficient Compression of Deep Networks via Convex Optimization

• URL: http://arxiv.org/abs/2410.06567v1
• Date: Wed, 9 Oct 2024 06:04:52 GMT
• Title: Convex Distillation: Efficient Compression of Deep Networks via Convex Optimization
• Authors: Prateek Varshney, Mert Pilanci,
• Abstract summary: Deployment of large and complex convex networks on resource-constrained devices significant challenges due to their demands. We introduce novel distillation technique that efficiently compresses model via this model via this paper. Our approach enables performance comparable to original model without requiring any post-processing.
• Score: 46.18363767705346
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Deploying large and complex deep neural networks on resource-constrained edge devices poses significant challenges due to their computational demands and the complexities of non-convex optimization. Traditional compression methods such as distillation and pruning often retain non-convexity that complicates fine-tuning in real-time on such devices. Moreover, these methods often necessitate extensive end-to-end network fine-tuning after compression to preserve model performance, which is not only time-consuming but also requires fully annotated datasets, thus potentially negating the benefits of efficient network compression. In this paper, we introduce a novel distillation technique that efficiently compresses the model via convex optimization -- eliminating intermediate non-convex activation functions and using only intermediate activations from the original model. Our approach enables distillation in a label-free data setting and achieves performance comparable to the original model without requiring any post-compression fine-tuning. We demonstrate the effectiveness of our method for image classification models on multiple standard datasets, and further show that in the data limited regime, our method can outperform standard non-convex distillation approaches. Our method promises significant advantages for deploying high-efficiency, low-footprint models on edge devices, making it a practical choice for real-world applications. We show that convex neural networks, when provided with rich feature representations from a large pre-trained non-convex model, can achieve performance comparable to their non-convex counterparts, opening up avenues for future research at the intersection of convex optimization and deep learning.

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