Comparer des méthodes

	MobileNet : Réseaux neuronaux convolutifs efficaces pour la vision mobile	Apprentissage par transfert
Domaine	Apprentissage profond	Apprentissage automatique
Famille	Machine learning	Machine learning
Année d'origine	2017	2010 (formalized); 1990s (early roots)
Auteur d'origine	Andrew Howard et al. (Google)	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Type	Lightweight CNN architecture	Learning paradigm
Source fondatrice	Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Alias	MobileNets, Depthwise Separable CNN, Efficient Mobile Vision Network, Mobil Evrişimli Sinir Ağı	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Apparentées	2	3
Résumé	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.	Transfer learning is a machine learning paradigm in which knowledge gained from training a model on a source task or domain is reused to improve learning on a different but related target task or domain. It is especially powerful when labeled data for the target task is scarce, and it underlies most modern deep learning applications in computer vision, natural language processing, and beyond.