imageNet Classification with Deep Convolutional Neural Networks

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Introduction

In this paper, they trained a large, deep neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. To learn about thousands of objects from millions of images, Convolutional Neural Network (CNN) is utilized due to its large learning capacity, fewer connections and parameters and outstanding performance on image classification.

Moreover, current GPU provides a powerful tool to facilitate the training of interestingly-large CNNs. Thus, they trained one of the largest convolutional neural networks to date on the datasets of ILSVRC-2010 and ILSVRC-2012 and achieved the best results ever reported on these datasets by the time this paper was written.

The code of their work is available here<ref> "High-performance C++/CUDA implementation of convolutional neural networks" </ref>.

Dataset

ImageNet Large-Scale Visual Recognition Challenge (ILSVRC) has roughly 1.2 million labeled high-resolution training images, 50 thousand validation images, and 150 thousand testing images over 1000 categories.

In this paper, the images in this dataset are down-sampled to a fixed resolution of 256 x 256. The only image pre-processing they used is subtracting the mean activity over the training set from each pixel.

Architecture

Reducing overfitting

Details of leaning

Results

Discussion

Bibliography

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