Vertaile menetelmiä

	MobileNet: Tehokkaat konvoluutionaaliset neuroverkot mobiilinäköön	Neuraaliarkkitehtuurihaku
Tieteenala	Syväoppiminen	Syväoppiminen
Menetelmäperhe	Machine learning	Machine learning
Syntyvuosi	2017	2017
Kehittäjä	Andrew Howard et al. (Google)	Zoph, B. & Le, Q.V.
Tyyppi	Lightweight CNN architecture	Automated architecture optimization (deep learning)
Alkuperäislähde	Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint.	Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR.
Rinnakkaisnimet	MobileNets, Depthwise Separable CNN, Efficient Mobile Vision Network, Mobil Evrişimli Sinir Ağı	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search
Liittyvät	2	5
Tiivistelmä	MobileNet is a family of lightweight convolutional neural network architectures introduced by Howard et al. at Google in 2017. It is designed to run image classification, object detection, and other vision tasks directly on mobile devices and embedded systems with limited computational budgets. By replacing standard convolutions with depthwise separable convolutions and exposing two global hyperparameters, MobileNet dramatically reduces multiply-add operations and model size while retaining competitive accuracy.	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.