论文标题
神经架构寻找反演
Neural Architecture Search for Inversion
论文作者
论文摘要
在一年中，人们一直在使用深度学习来解决反转问题，我们看到该框架已用于建立录制波场和速度之间的关系（Yang等，2016）。在这里，我们将从两个角度扩展工作，一个正在得出更合适的损失功能，因为现在，Pixel-2像素比较可能不是表征图像结构的最佳选择，我们将详细介绍如何构建成本函数以捕获高级功能以增强模型性能。另一个维度是搜索更合适的神经体系结构，这是一个更大图片的子集，自动机器学习或汽车。有几个著名的网络，U-Net，Resnet（He等，2016）和Densenet（Huang等，2017），它们在某些问题上取得了惊人的结果，但是很难说它们是倒置问题的最佳选择，而无需在某些空间内进行彻底搜索。在这里，我们将展示我们的架构搜索结果的反演。
Over the year, people have been using deep learning to tackle inversion problems, and we see the framework has been applied to build relationship between recording wavefield and velocity (Yang et al., 2016). Here we will extend the work from 2 perspectives, one is deriving a more appropriate loss function, as we now, pixel-2-pixel comparison might not be the best choice to characterize image structure, and we will elaborate on how to construct cost function to capture high level feature to enhance the model performance. Another dimension is searching for the more appropriate neural architecture, which is a subset of an even bigger picture, the automatic machine learning, or AutoML. There are several famous networks, U-net, ResNet (He et al., 2016) and DenseNet (Huang et al., 2017), and they achieve phenomenal results for certain problems, yet it's hard to argue they are the best for inversion problems without thoroughly searching within certain space. Here we will be showing our architecture search results for inversion.
