Bandingkan metode

	EfficientNet	Pencarian Arsitektur Neural
Bidang	Pembelajaran Mendalam	Pembelajaran Mendalam
Keluarga	Machine learning	Machine learning
Tahun asal	2019	2017
Pencetus	Tan, M. & Le, Q. V.	Zoph, B. & Le, Q.V.
Tipe	Compound-scaled convolutional neural network architecture	Automated architecture optimization (deep learning)
Sumber perintis	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.
Alias	EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2	Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search
Terkait	4	5
Ringkasan	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.