Convolutional Neural Networks

GoogLeNet

Introduced by Szegedy et al. in Going Deeper with Convolutions

GoogLeNet is a type of convolutional neural network based on the Inception architecture. It utilises Inception modules, which allow the network to choose between multiple convolutional filter sizes in each block. An Inception network stacks these modules on top of each other, with occasional max-pooling layers with stride 2 to halve the resolution of the grid.

Source: Going Deeper with Convolutions

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
General Classification 25 15.63%
Image Classification 22 13.75%
Object Detection 13 8.13%
Quantization 12 7.50%
Object Recognition 7 4.38%
Domain Adaptation 5 3.13%
Semantic Segmentation 3 1.88%
Face Recognition 3 1.88%
Model Compression 3 1.88%

Categories