Porovnať metódy

	DenseNet	EfficientNet
Odbor	Hlboké učenie	Hlboké učenie
Rodina	Machine learning	Machine learning
Rok vzniku	2017	2019
Tvorca	Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q.	Tan, M. & Le, Q. V.
Typ	Dense convolutional neural network (feed-forward dense connectivity)	Compound-scaled convolutional neural network architecture
Pôvodný zdroj	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.	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.
Ďalšie názvy	DenseNet, Dense Convolutional Network, densely connected CNN, DenseNet-121	EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2
Príbuzné	2	4
Zhrnutie	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.