So sánh phương pháp
Xem các phương pháp đã chọn cạnh nhau; những hàng khác biệt được làm nổi bật.
| MobileNet: Mạng nơ-ron tích chập hiệu quả cho thị giác di động× | Tìm kiếm Kiến trúc Mạng Nơ-ron× | |
|---|---|---|
| Lĩnh vực | Học sâu | Học sâu |
| Họ | Machine learning | Machine learning |
| Năm ra đời | 2017 | 2017 |
| Người khởi xướng≠ | Andrew Howard et al. (Google) | Zoph, B. & Le, Q.V. |
| Loại≠ | Lightweight CNN architecture | Automated architecture optimization (deep learning) |
| Công trình gốc≠ | 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 ↗ |
| Tên gọi khác | MobileNets, Depthwise Separable CNN, Efficient Mobile Vision Network, Mobil Evrişimli Sinir Ağı | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search |
| Liên quan≠ | 2 | 5 |
| Tóm tắt≠ | 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. |
| ScholarGateBộ dữ liệu ↗ |
|
|