The paper "ImageNet Classification with Deep Convolutional Neural Networks" by Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton presents a large, deep convolutional neural network (CNN) trained to classify 1.2 million high-resolution images from the ImageNet ILSVRC-2010 contest into 1000 classes. The network, consisting of five convolutional layers and three fully connected layers, achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, on the test data, significantly outperforming previous state-of-the-art methods. The authors used non-saturating neurons and efficient GPU implementations to speed up training, and employed dropout regularization to reduce overfitting. They also entered a variant of their model in the ILSVRC-2012 competition, achieving a winning top-5 test error rate of 15.3%. The paper discusses the importance of network depth, the use of ReLU nonlinearity, data augmentation, and other techniques to improve performance and reduce overfitting. The results demonstrate the feasibility of using deep CNNs for large-scale image classification tasks.The paper "ImageNet Classification with Deep Convolutional Neural Networks" by Alex Krizhevsky, Ilya Sutskever, and Geoffrey E. Hinton presents a large, deep convolutional neural network (CNN) trained to classify 1.2 million high-resolution images from the ImageNet ILSVRC-2010 contest into 1000 classes. The network, consisting of five convolutional layers and three fully connected layers, achieved top-1 and top-5 error rates of 37.5% and 17.0%, respectively, on the test data, significantly outperforming previous state-of-the-art methods. The authors used non-saturating neurons and efficient GPU implementations to speed up training, and employed dropout regularization to reduce overfitting. They also entered a variant of their model in the ILSVRC-2012 competition, achieving a winning top-5 test error rate of 15.3%. The paper discusses the importance of network depth, the use of ReLU nonlinearity, data augmentation, and other techniques to improve performance and reduce overfitting. The results demonstrate the feasibility of using deep CNNs for large-scale image classification tasks.