[Classic] ImageNet Classification with Deep Convolutional Neural Networks (Paper Explained)
1,066#ai #research #alexnet
AlexNet was the start of the deep learning revolution. Up until 2012, the best computer vision systems relied on hand-crafted features and highly specialized algorithms to perform object classification. This paper was the first to successfully train a deep convolutional neural network on not one, but two GPUs and managed to outperform the competition on ImageNet by an order of magnitude.
OUTLINE:
0:00 - Intro & Overview
2:00 - The necessity of larger models
6:20 - Why CNNs?
11:05 - ImageNet
12:05 - Model Architecture Overview
14:35 - ReLU Nonlinearities
18:45 - Multi-GPU training
21:30 - Classification Results
24:30 - Local Response Normalization
28:05 - Overlapping Pooling
32:25 - Data Augmentation
38:30 - Dropout
40:30 - More Results
43:50 - Conclusion
Paper: http://www.cs.toronto.edu/~hinton/absps/imagenet.pdf
Abstract:
We trained a large, deep convolutional neural network to classify the 1.2 million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 37.5% and 17.0% which is considerably better than the previous state-of-the-art. The neural network, which has 60 million parameters and 650,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and three fully-connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of the convolution operation. To reduce overfitting in the fully-connected layers we employed a recently-developed regularization method called “dropout” that proved to be very effective. We also entered a variant of this model in the ILSVRC-2012 competition and achieved a winning top-5 test error rate of 15.3%, compared to 26.2% achieved by the second-best entry.
Authors: Alex Krizhevsky, Ilya Sutskever, Geoffrey E. Hinton
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