Methoden vergelijken
Bekijk de geselecteerde methoden naast elkaar; rijen die verschillen zijn gemarkeerd.
| AlexNet× | DenseNet× | EfficientNet× | |
|---|---|---|---|
| Vakgebied | Deep learning | Deep learning | Deep learning |
| Familie | Machine learning | Machine learning | Machine learning |
| Jaar van ontstaan≠ | 2012 | 2017 | 2019 |
| Grondlegger≠ | Krizhevsky, A.; Sutskever, I.; Hinton, G. E. | Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q. | Tan, M. & Le, Q. V. |
| Type≠ | Deep Convolutional Neural Network (CNN) | Dense convolutional neural network (feed-forward dense connectivity) | Compound-scaled convolutional neural network architecture |
| Oorspronkelijke bron≠ | 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 ↗ | 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 ↗ |
| Aliassen≠ | AlexNet, Krizhevsky net, SuperVision CNN, ImageNet CNN 2012 | DenseNet, Dense Convolutional Network, densely connected CNN, DenseNet-121 | EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2 |
| Verwant≠ | 3 | 2 | 4 |
| Samenvatting≠ | 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. | 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. |
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