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Deep Learning

Deep Learning: Understanding The Inception Module

An article on a Deep Learning Architecture Inspired By An Internet Meme — and its technical information and details.

Introduction

Back in 2014, researchers at Google (and other research institutions) published a paper that introduced a novel deep learning convolutional neural network architecture that was, at the time, the largest and most efficient deep neural network architecture.

The novel architecture was an Inception Network, and a variant of this Network called, GoogLeNet went on to achieve the state of the art performance in the classification computer vision task of the ImageNet LargeScale Visual Recognition Challenge 2014(ILVRC14).

We’ve come a long way from 2014, and so has the deep learning field. In 2020 several deep learning architectures achieve and exceed human-level performance in classification and object detection tasks.

However, the innovations and improvements within current convolutional neural networks have their roots set in their predecessors.

What to expect: This article explores the integral component of the Inception Network, this integral component is the Inception Module.