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| EfficientNet× | Neuronale Architektursuche× | |
|---|---|---|
| Fachgebiet | Deep Learning | Deep Learning |
| Familie | Machine learning | Machine learning |
| Entstehungsjahr≠ | 2019 | 2017 |
| Urheber≠ | Tan, M. & Le, Q. V. | Zoph, B. & Le, Q.V. |
| Typ≠ | Compound-scaled convolutional neural network architecture | Automated architecture optimization (deep learning) |
| Wegweisende Quelle≠ | 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. link ↗ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link ↗ |
| Aliasnamen | EfficientNet, compound scaling CNN, EfficientNet-B0 through B7, EfficientNetV2 | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search |
| Verwandt≠ | 4 | 5 |
| Zusammenfassung≠ | 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. |
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