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Compressing Neural Networks using the Variational Information Bottelneck
Bin Dai · Chen Zhu · Baining Guo · David Wipf

Thu Jul 12 09:15 AM -- 12:00 PM (PDT) @ Hall B #128

Neural networks can be compressed to reduce memory and computational requirements, or to increase accuracy by facilitating the use of a larger base architecture. In this paper we focus on pruning individual neurons, which can simultaneously trim model size, FLOPs, and run-time memory. To improve upon the performance of existing compression algorithms we utilize the information bottleneck principle instantiated via a tractable variational bound. Minimization of this information theoretic bound reduces the redundancy between adjacent layers by aggregating useful information into a subset of neurons that can be preserved. In contrast, the activations of disposable neurons are shut off via an attractive form of sparse regularization that emerges naturally from this framework, providing tangible advantages over traditional sparsity penalties without contributing additional tuning parameters to the energy landscape. We demonstrate state-of-the-art compression rates across an array of datasets and network architectures.

Author Information

Bin Dai (Tsinghua University)
Chen Zhu (University of Maryland)
Baining Guo (MSR Asia)
David Wipf (Microsoft Research)

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