Võrdle meetodeid
Vaata valitud meetodeid kõrvuti; erinevad read on esile tõstetud.
| AlexNet× | DenseNet× | |
|---|---|---|
| Valdkond | Süvaõpe | Süvaõpe |
| Perekond | Machine learning | Machine learning |
| Tekkeaasta≠ | 2012 | 2017 |
| Looja≠ | Krizhevsky, A.; Sutskever, I.; Hinton, G. E. | Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q. |
| Tüüp≠ | Deep Convolutional Neural Network (CNN) | Dense convolutional neural network (feed-forward dense connectivity) |
| Algallikas≠ | 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 ↗ | Huang, G., Liu, Z., van der Maaten, L., & Weinberger, K. Q. (2017). Densely Connected Convolutional Networks. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 4700–4708. DOI ↗ |
| Rööpnimetused≠ | AlexNet, Krizhevsky net, SuperVision CNN, ImageNet CNN 2012 | DenseNet, Dense Convolutional Network, densely connected CNN, DenseNet-121 |
| Seotud≠ | 3 | 2 |
| Kokkuvõte≠ | 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. | DenseNet (Densely Connected Convolutional Network), introduced by Huang, Liu, van der Maaten, and Weinberger at CVPR 2017 (Best Paper Award), connects every layer to every subsequent layer within a dense block so that each layer receives the concatenated feature maps of all preceding layers — maximising feature reuse, strengthening gradient flow, and achieving competitive accuracy with substantially fewer parameters than comparable architectures such as ResNet. |
| ScholarGateAndmestik ↗ |
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