Convolutional Neural Networks

Inception-v3

Introduced by Szegedy et al. in Rethinking the Inception Architecture for Computer Vision

Inception-v3 is a convolutional neural network architecture from the Inception family that makes several improvements including using Label Smoothing, Factorized 7 x 7 convolutions, and the use of an auxiliary classifer to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead).

Source: Rethinking the Inception Architecture for Computer Vision

Papers


Paper Code Results Date Stars

Tasks


Task Papers Share
Image Classification 15 17.05%
General Classification 15 17.05%
Adversarial Attack 5 5.68%
Quantization 4 4.55%
Semantic Segmentation 3 3.41%
Management 3 3.41%
Face Recognition 2 2.27%
Object Detection 2 2.27%
Image Captioning 2 2.27%

Categories