Compressing Neural Networks using the Variational Information Bottleneck (1802.10399v3)

Abstract: 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.

• The paper introduces a neural network compression framework utilizing the variational information bottleneck to balance model complexity and performance by retaining only informative components.
• The approach formulates compression as an optimization problem solved via variational inference, demonstrating higher compression ratios and robustness across network types compared to traditional methods.
• This research offers practical benefits for deploying AI on constrained devices like mobile phones and theoretical advancements for future information-theoretic approaches in neural network optimization.

Compressing Neural Networks using the Variational Information Bottleneck

The paper "Compressing Neural Networks using the Variational Information Bottleneck" by Bin Dai, Chen Zhu, and David Wipf presents a sophisticated framework for neural network compression based on the principles of the variational information bottleneck (VIB). This work addresses the computational and energy efficiency challenges associated with the deployment of modern large-scale neural networks. By leveraging the VIB paradigm, the authors propose a methodology that identifies and retains the most informative components of a neural network while eliminating redundant ones.

The core concept is to maintain an optimal compromise between model capacity and compression, ensuring minimal loss of accuracy. The VIB framework is utilized to model the trade-off between compression rate and prediction precision, dictating the network's architecture and functionality through an information-theoretic framework.

A significant contribution of this work is the formulation of the compression process as an optimization problem where the objective is to maximize mutual information between compressed representations and output while minimizing mutual information between input and compressed representations. The authors strategically apply variational inference techniques to approximate the optimal compression strategy. Empirical results included in the paper demonstrate that the proposed approach significantly reduces model complexity while retaining or even enhancing predictive performance across several benchmark datasets.

Quantitative findings reveal that the approach achieves substantial reductions in model size, with compression ratios exceeding those of traditional pruning and quantization methods. Additionally, the approach exhibits robustness across different types of neural network architectures, including convolutional and fully connected networks, indicating its broad applicability.

The implications of this research are multifaceted, suggesting practical benefits for deploying artificial intelligence in constrained environments such as mobile devices and embedded systems. Theoretically, this paper paves the way for future exploration into information-theoretic measures in the field of neural network optimization. It propels the understanding and implementation of efficient AI models by emphasizing compression without loss of critical information, offering promising directions for reducing the ecological impact of AI computations.

Future developments anticipated from this research might involve adapting the VIB framework to address other model optimization problems, such as improving generalization or transfer learning capabilities. Additionally, further exploration into the integration of VIB with other advanced compression techniques may enhance the overall efficacy and adaptability, contributing to a more sustainable trajectory of AI technology development.