Linganisha mbinu
Pitia mbinu ulizochagua bega kwa bega; safu zinazotofautiana zinaangaziwa.
| MobileNet: Mitandao ya Mawasiliano ya Kina kwa Maono ya Simu× | Utafutaji wa Usanifu wa Neural× | |
|---|---|---|
| Nyanja | Ujifunzaji wa Kina | Ujifunzaji wa Kina |
| Familia | Machine learning | Machine learning |
| Mwaka wa asili | 2017 | 2017 |
| Mwanzilishi≠ | Andrew Howard et al. (Google) | Zoph, B. & Le, Q.V. |
| Aina≠ | Lightweight CNN architecture | Automated architecture optimization (deep learning) |
| Chanzo asilia≠ | Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint. link ↗ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. link ↗ |
| Majina mbadala | MobileNets, Depthwise Separable CNN, Efficient Mobile Vision Network, Mobil Evrişimli Sinir Ağı | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search |
| Zinazohusiana≠ | 2 | 5 |
| Muhtasari≠ | 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. | 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. |
| ScholarGateSeti ya data ↗ |
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