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arXiv · 2205.06404

Fast Conditional Network Compression Using Bayesian HyperNetworks

Phuoc Nguyen·Truyen Tran·Ky Le·Sunil Gupta·Santu Rana·Dang Nguyen·Trong Nguyen·Shannon Ryan·Svetha Venkatesh
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Abstract

We introduce a conditional compression problem and propose a fast framework for tackling it. The problem is how to quickly compress a pretrained large neural network into optimal smaller networks given target contexts, e.g. a context involving only a subset of classes or a context where only limited compute resource is available. To solve this, we propose an efficient Bayesian framework to compress a given large network into much smaller size tailored to meet each contextual requirement. We employ a hypernetwork to parameterize the posterior distribution of weights given conditional inputs and minimize a variational objective of this Bayesian neural network. To further reduce the network sizes, we propose a new input-output group sparsity factorization of weights to encourage more sparseness in the generated weights. Our methods can quickly generate compressed networks with significantly smaller sizes than baseline methods.

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Phuoc Nguyen, Truyen Tran, Ky Le, Sunil Gupta, Santu Rana, Dang Nguyen, Trong Nguyen, Shannon Ryan, Svetha Venkatesh. 2022-05-13. Fast Conditional Network Compression Using Bayesian HyperNetworks. https://arxiv.org/abs/2205.06404

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