Transcription of ImageNet Classification with Deep Convolutional Neural …
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ImageNet Classification with Deep Convolutional Neural …
ImageNet Classification with Deep Convolutional Neural Networks Alex Krizhevsky Ilya Sutskever Geoffrey E. Hinton University of Toronto University of Toronto University of Toronto Abstract We trained a large, deep Convolutional Neural network to classify the million high-resolution images in the ImageNet LSVRC-2010 contest into the 1000 dif- ferent classes. On the test data, we achieved top-1 and top-5 error rates of and 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.
Current approaches to object recognition make essential use of machine learning methods. To im-prove their performance, we can collect larger datasets, learn more powerful models, and use bet-ter techniques for preventing overfitting. Until recently, datasets of …
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