পদ্ধতির তুলনা করুন

	মোবাইলনেট: মোবাইলের জন্য কার্যকর কনভোল্যুশনাল নিউরাল নেটওয়ার্ক	নিউরাল আর্কিটেকচার সার্চ
ক্ষেত্র	গভীর শিখন	গভীর শিখন
পরিবার	Machine learning	Machine learning
উদ্ভবের বছর	2017	2017
প্রবর্তক	Andrew Howard et al. (Google)	Zoph, B. & Le, Q.V.
ধরন	Lightweight CNN architecture	Automated architecture optimization (deep learning)
মৌলিক উৎস	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.
অপর নাম	MobileNets, Depthwise Separable CNN, Efficient Mobile Vision Network, Mobil Evrişimli Sinir Ağı	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search
সম্পর্কিত	2	5
সারসংক্ষেপ	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.