手法を比較

	AlexNet	EfficientNet
分野	深層学習	深層学習
系統	Machine learning	Machine learning
提唱年	2012	2019
提唱者	Krizhevsky, A.; Sutskever, I.; Hinton, G. E.	Tan, M. & Le, Q. V.
種類	Deep Convolutional Neural Network (CNN)	Compound-scaled convolutional neural network architecture
原典	Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2012). ImageNet Classification with Deep Convolutional Neural Networks. Advances in Neural Information Processing Systems, 25, 1097–1105. (Republished: Communications of the ACM, 60(6), 84–90, 2017.)	Tan, M. & Le, Q. V. (2019). EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks. Proceedings of the 36th International Conference on Machine Learning (ICML 2019), PMLR 97, 6105–6114.
別名	AlexNet, Krizhevsky net, SuperVision CNN, ImageNet CNN 2012	EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2
関連	3	4
概要	AlexNet is a deep convolutional neural network (CNN) introduced by Alex Krizhevsky, Ilya Sutskever, and Geoffrey Hinton in 2012. It won the ImageNet Large Scale Visual Recognition Challenge (ILSVRC 2012) with a top-5 error rate of 15.3%, outstripping the runner-up by more than 10 percentage points and reigniting broad interest in deep learning. The architecture introduced or popularised several techniques — ReLU activations, dropout regularisation, and multi-GPU training — that became standard practice across the field.	EfficientNet is a family of convolutional neural network architectures introduced by Mingxing Tan and Quoc V. Le (Google Brain) at ICML 2019 that systematically co-scales network depth, width, and input resolution using a single compound coefficient, achieving state-of-the-art image classification accuracy with substantially fewer parameters and FLOPs than prior networks such as ResNet and Inception.