手法を比較

	モバイルネット：モバイルビジョン向け効率的な畳み込みニューラルネットワーク	ResNet（Residual Network）	転移学習
分野	深層学習	深層学習	機械学習
系統	Machine learning	Machine learning	Machine learning
提唱年	2017	2016	2010 (formalized); 1990s (early roots)
提唱者	Andrew Howard et al. (Google)	He, K.; Zhang, X.; Ren, S.; Sun, J.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
種類	Lightweight CNN architecture	Deep Convolutional Neural Network with skip connections	Learning paradigm
原典	Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint.	He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
別名	MobileNets, Depthwise Separable CNN, Efficient Mobile Vision Network, Mobil Evrişimli Sinir Ağı	ResNet, Residual Network, Deep Residual Learning, ResNet-50	TL, domain adaptation, fine-tuning, pre-trained model adaptation
関連	2	4	3
概要	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.	ResNet (Residual Network) is a deep convolutional neural network architecture introduced by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun at CVPR 2016. By inserting shortcut (skip) connections that carry the input of a block directly to its output — defining the block's task as learning a residual correction rather than a full mapping — ResNet enabled training of networks with hundreds or even thousands of layers without the vanishing-gradient degradation that had previously made very deep networks impractical. It won the ILSVRC 2015 image recognition competition with a top-5 error of 3.57% and remains the most widely used backbone architecture in computer vision.	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.