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	EfficientNet	Neúrové vyhľadávanie architektúry
Odbor	Hlboké učenie	Hlboké učenie
Rodina	Machine learning	Machine learning
Rok vzniku	2019	2017
Tvorca	Tan, M. & Le, Q. V.	Zoph, B. & Le, Q.V.
Typ	Compound-scaled convolutional neural network architecture	Automated architecture optimization (deep learning)
Pôvodný zdroj	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.	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.
Ďalšie názvy	EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search
Príbuzné	4	5
Zhrnutie	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.	Neural Architecture Search (NAS), introduced by Zoph and Le in 2017, automatically optimizes architectural decisions such as a network's depth, width, and connection structure instead of hand-designing them. Leading methods in the field include DARTS, ENAS, and Once-for-All.