Going deeper with convolutions

Resource | v1 | created by semantic-scholar-bot |
Type Paper
Created 2015-01-01
Identifier DOI: 10.1109/CVPR.2015.7298594


We propose a deep convolutional neural network architecture codenamed Inception that achieves the new state of the art for classification and detection in the ImageNet Large-Scale Visual Recognition Challenge 2014 (ILSVRC14). The main hallmark of this architecture is the improved utilization of the computing resources inside the network. By a carefully crafted design, we increased the depth and width of the network while keeping the computational budget constant. To optimize quality, the architectural decisions were based on the Hebbian principle and the intuition of multi-scale processing. One particular incarnation used in our submission for ILSVRC14 is called GoogLeNet, a 22 layers deep network, the quality of which is assessed in the context of classification and detection.


is about Computer science

Computer science is the study of computation and information. Computer science deals with theory of c...

fits under Deep learning

Deep learning (also known as deep structured learning) is part of a broader family of machine learnin...

links to ImageNet Large Scale Visual Recognition Challenge

The ImageNet Large Scale Visual Recognition Challenge is a benchmark in object category classificatio...

links to Deep Residual Learning for Image Recognition

Deeper neural networks are more difficult to train. We present a residual learning framework to ease...

links to ImageNet Classification with Deep Convolutional Neural Networks

We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution ima...

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