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| AlexNet× | EfficientNet× | |
|---|---|---|
| 분야 | 딥러닝 | 딥러닝 |
| 계열 | Machine learning | Machine learning |
| 기원 연도≠ | 2012 | 2019 |
| 창시자≠ | Krizhevsky, A.; Sutskever, I.; Hinton, G. E. | Tan, M. & Le, Q. V. |
| 유형≠ | Deep Convolutional Neural Network (CNN) | Compound-scaled convolutional neural network architecture |
| 원전≠ | 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.) DOI ↗ | 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. link ↗ |
| 별칭≠ | AlexNet, Krizhevsky net, SuperVision CNN, ImageNet CNN 2012 | EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2 |
| 관련≠ | 3 | 4 |
| 요약≠ | 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. |
| ScholarGate데이터셋 ↗ |
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