Self-supervised learning with physics-aware neural networks – I. Galaxy model fitting
Self-supervised learning with physics-aware neural networks – I. Galaxy model fitting
复制标题
使用物理感知神经网络进行自我监督学习 – I. Galaxy 模型拟合
DOI：
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发表时间：
2019
影响因子：
4.8
通讯作者：
M. Aragon
中科院分区：
文献类型：
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作者：
M. Aragon
使用神经网络估计描述一组观测的模型的参数通常以监督的方式解决。如果我们无法访问模型的真实参数，则无法应用这种方法。另一方面，标准的无监督学习技术不会产生与模型参数相关的有意义或语义表示。在这里，我们介绍了一种新的自监督混合网络架构，结合传统的神经网络元素与分析或数值模型，这代表了一个物理过程，由系统学习。自我监督学习是通过生成一个相当于物理模型参数的内部表示来实现的。这种语义表示用于评估模型，并在训练期间将其与输入数据进行比较。这里描述的语义自动编码器架构共享神经网络的鲁棒性，同时包括数据的显式模型，以无监督的方式学习，并通过构造直接物理解释来估计参数。作为一个说明性应用，我们对指数光分布的2D模型拟合进行无监督学习，并根据网络大小和噪声来评估网络的性能。
Estimating the parameters of a model describing a set of observations using a neural network is, in general, solved in a supervised way. In cases when we do not have access to the model’s true parameters, this approach can not be applied. Standard unsupervised learning techniques, on the other hand, do not produce meaningful or semantic representations that can be associated with the model’s parameters. Here we introduce a novel self-supervised hybrid network architecture that combines traditional neural network elements with analytic or numerical models, which represent a physical process to be learned by the system. Self-supervised learning is achieved by generating an internal representation equivalent to the parameters of the physical model. This semantic representation is used to evaluate the model and compare it to the input data during training. The semantic autoencoder architecture described here shares the robustness of neural networks while including an explicit model of the data, learns in an unsupervised way, and estimates, by construction, parameters with direct physical interpretation. As an illustrative application, we perform unsupervised learning for 2D model fitting of exponential light profiles and evaluate the performance of the network as a function of network size and noise.