Compressing Convolutional Neural Networks

TL;DR

This paper introduces FreshNets, a novel CNN compression method that leverages frequency domain analysis and hashing to significantly reduce model size while maintaining performance.

Contribution

The paper proposes a new architecture, FreshNets, that exploits frequency domain properties and hashing to compress CNNs more effectively than existing methods.

Findings

FreshNets achieve higher compression ratios with minimal accuracy loss.

Frequency-based hashing effectively captures redundancy in CNN weights.

Fewer parameters are allocated to high-frequency components, optimizing compression.

Abstract

Convolutional neural networks (CNN) are increasingly used in many areas of computer vision. They are particularly attractive because of their ability to "absorb" great quantities of labeled data through millions of parameters. However, as model sizes increase, so do the storage and memory requirements of the classifiers. We present a novel network architecture, Frequency-Sensitive Hashed Nets (FreshNets), which exploits inherent redundancy in both convolutional layers and fully-connected layers of a deep learning model, leading to dramatic savings in memory and storage consumption. Based on the key observation that the weights of learned convolutional filters are typically smooth and low-frequency, we first convert filter weights to the frequency domain with a discrete cosine transform (DCT) and use a low-cost hash function to randomly group frequency parameters into hash buckets. All parameters assigned the same hash bucket share a single value learned with standard back-propagation. To further reduce model size we allocate fewer hash buckets to high-frequency components, which are generally less important. We evaluate FreshNets on eight data sets, and show that it leads to drastically better compressed performance than several relevant baselines.