Understanding Convolutional Neural Networks

TL;DR

This paper reviews the mathematical properties of CNNs, providing insights into why they perform well despite limited understanding, and summarizes key theoretical results to enhance intuition about their operation.

Contribution

It compiles and explains core theoretical findings about CNNs, offering a clearer understanding of their mathematical behavior and effectiveness.

Findings

Summarizes key theoretical results on CNNs.

Provides intuition for CNNs' success.

Highlights gaps in current understanding.

Abstract

Convoulutional Neural Networks (CNNs) exhibit extraordinary performance on a variety of machine learning tasks. However, their mathematical properties and behavior are quite poorly understood. There is some work, in the form of a framework, for analyzing the operations that they perform. The goal of this project is to present key results from this theory, and provide intuition for why CNNs work.