方法对比
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| 神经架构搜索× | 残差网络（ResNet）× | |
|---|---|---|
| 领域 | 深度学习 | 深度学习 |
| 方法族 | Machine learning | Machine learning |
| 起源年份≠ | 2017 | 2016 |
| 提出者≠ | Zoph, B. & Le, Q.V. | He, K.; Zhang, X.; Ren, S.; Sun, J. |
| 类型≠ | Automated architecture optimization (deep learning) | Deep Convolutional Neural Network with skip connections |
| 开创性文献≠ | Zoph, B. & Le, Q.V. (2017). Neural Architecture Search with Reinforcement Learning. ICLR. 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 ↗ |
| 别名≠ | Nöral Mimari Arama (NAS), NAS, automated architecture design, differentiable architecture search | ResNet, Residual Network, Deep Residual Learning, ResNet-50 |
| 相关≠ | 5 | 4 |
| 摘要≠ | 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. | 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. |
| ScholarGate数据集 ↗ |
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