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| MobileNet: Wydajne konwolucyjne sieci neuronowe dla wizji mobilnej× | ResNet (Residual Network)× | |
|---|---|---|
| Dziedzina | Uczenie głębokie | Uczenie głębokie |
| Rodzina | Machine learning | Machine learning |
| Rok powstania≠ | 2017 | 2016 |
| Twórca≠ | Andrew Howard et al. (Google) | He, K.; Zhang, X.; Ren, S.; Sun, J. |
| Typ≠ | Lightweight CNN architecture | Deep Convolutional Neural Network with skip connections |
| Źródło pierwotne≠ | Howard, A. G., et al. (2017). MobileNets: Efficient convolutional neural networks for mobile vision applications. arXiv preprint. link ↗ | He, K., Zhang, X., Ren, S., & Sun, J. (2016). Deep Residual Learning for Image Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 770–778. DOI ↗ |
| Inne nazwy≠ | MobileNets, Depthwise Separable CNN, Efficient Mobile Vision Network, Mobil Evrişimli Sinir Ağı | ResNet, Residual Network, Deep Residual Learning, ResNet-50 |
| Pokrewne≠ | 2 | 4 |
| Podsumowanie≠ | 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. | ResNet (Residual Network) is a deep convolutional neural network architecture introduced by Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun at CVPR 2016. By inserting shortcut (skip) connections that carry the input of a block directly to its output — defining the block's task as learning a residual correction rather than a full mapping — ResNet enabled training of networks with hundreds or even thousands of layers without the vanishing-gradient degradation that had previously made very deep networks impractical. It won the ILSVRC 2015 image recognition competition with a top-5 error of 3.57% and remains the most widely used backbone architecture in computer vision. |
| ScholarGateZbiór danych ↗ |
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