Krizhevsky, AlexSutskever, IlyaHinton, Geoffrey E.2025-06-022025-06-022025-06-022012-12-03https://proceedings.neurips.cc/paper_files/paper/2012/file/c399862d3b9d6b76c8436e924a68c45b-Paper.pdfhttp://data.inu.ac.kr/handle/123456789/1933.3AlexNet은 1.2백만 개의 ImageNet 이미지에 대해 대규모 CNN을 학습하여 당시 최첨단 수준의 분류 성능을 달성하였으며, 딥러닝 시대를 열었습니다 ©2012 NeurIPS AuthorsWe trained a large, deep convolutional neural network to classify the 1.3 million high-resolution images in the LSVRC-2010 ImageNet training set into the 1000 different classes. On the test data, we achieved top-1 and top-5 error rates of 39.7\% and 18.9\% which is considerably better than the previous state-of-the-art results. The neural network, which has 60 million parameters and 500,000 neurons, consists of five convolutional layers, some of which are followed by max-pooling layers, and two globally connected layers with a final 1000-way softmax. To make training faster, we used non-saturating neurons and a very efficient GPU implementation of convolutional nets. To reduce overfitting in the globally connected layers we employed a new regularization method that proved to be very effective.en-USArticle