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	EfficientNet	Ricerca Architetturale Neurale	Apprendimento per trasferimento
Campo	Apprendimento profondo	Apprendimento profondo	Apprendimento automatico
Famiglia	Machine learning	Machine learning	Machine learning
Anno di origine	2019	2017	2010 (formalized); 1990s (early roots)
Ideatore	Tan, M. & Le, Q. V.	Zoph, B. & Le, Q.V.	Pan, S. J. & Yang, Q. (survey); Bengio, Y. (deep learning framing)
Tipo	Compound-scaled convolutional neural network architecture	Automated architecture optimization (deep learning)	Learning paradigm
Fonte seminale	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.	Pan, S. J., & Yang, Q. (2010). A Survey on Transfer Learning. IEEE Transactions on Knowledge and Data Engineering, 22(10), 1345–1359.
Alias	EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search	TL, domain adaptation, fine-tuning, pre-trained model adaptation
Correlati	4	5	3
Sintesi	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.	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.