Compressing Convolutional Neural Networks

Understand

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.