Vertaile menetelmiä

	DenseNet	EfficientNet
Tieteenala	Syväoppiminen	Syväoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2017	2019
Kehittäjä	Huang, G.; Liu, Z.; van der Maaten, L.; Weinberger, K. Q.	Tan, M. & Le, Q. V.
Tyyppi	Dense convolutional neural network (feed-forward dense connectivity)	Compound-scaled convolutional neural network architecture
Alkuperäislähde	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.
Rinnakkaisnimet	DenseNet, Dense Convolutional Network, densely connected CNN, DenseNet-121	EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2
Liittyvät	2	4
Tiivistelmä	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.