Inception Net

Definition

Inception Net model is a deep Convolutional Neural Network architecture using the inception module.

Architecture

Inception Net V1

Inception Module

Inception module is a building block of the inception net. It uses multiple filter sizes ($1\times 1$, $3\times 3$, and $5\times 5$) and pooling operations in parallel, allowing the network to capture features at different scales simultaneously.

The $\mathrm{conv}(1\times 1)$ are used for dimensionality reduction, helping to reduce computational complexity.

Inception Net V2, V3

Factorized Convolution

The large convolutions in the inception module were replaced with multiple smaller convolutions $\mathrm{conv}(5\times 5)\to \mathrm{conv}(3\times 3)\ast 2$ reducing parameters and computational cost.

Asymmetric Convolution

$\mathrm{conv}(n\times n)$ are decomposed into $\mathrm{conv}(1\times n)$ and $\mathrm{conv}(n\times 1)$ convolution.

Label Smoothing

The model prevent from becoming overconfident applying the label smoothing to the labels. $y_i^{\prime }=(1-ϵ)y_i+\frac{ϵ}{K}$ where $y_i^{\prime }$ is the smoothed label, $y_i$ is the original one-hot encoded label, $ϵ$ is the smoothing parameter, and $K$ is the number of classes.