论文标题
使用深层神经网络来注释大量天文图像目录时，系统偏见会发生
Systematic biases when using deep neural networks for annotating large catalogs of astronomical images
论文作者
论文摘要
深度卷积神经网络（DCNN）由于其非参数性质，良好的性能及其通过TensorFlow等库的可访问性而成为最常见的自动图像注释解决方案。在其他领域中，DCNN也是数字天空调查获得的大型天文图像数据库注释的常见方法。 DCNNS的主要弊端之一是使DCNNS充当``黑匣子''的复杂非直觉规则，以不清楚用户不清楚的方式提供注释。因此，用户通常无法知道DCNN用于分类。在这里我们表明训练dcnn的训练是在训练中的训练，即训练dcnn是一个敏感的训练。表明，对于椭圆形和螺旋星系的基本分类，用于训练的星系的天空位置会影响算法的行为，并导致偏见很小但一致且具有统计学意义的偏见，以宇宙学量表的形式表现出来。对于星系的形态，应考虑到物体的视觉外观更多的方面。
Deep convolutional neural networks (DCNNs) have become the most common solution for automatic image annotation due to their non-parametric nature, good performance, and their accessibility through libraries such as TensorFlow. Among other fields, DCNNs are also a common approach to the annotation of large astronomical image databases acquired by digital sky surveys. One of the main downsides of DCNNs is the complex non-intuitive rules that make DCNNs act as a ``black box", providing annotations in a manner that is unclear to the user. Therefore, the user is often not able to know what information is used by the DCNNs for the classification. Here we demonstrate that the training of a DCNN is sensitive to the context of the training data such as the location of the objects in the sky. We show that for basic classification of elliptical and spiral galaxies, the sky location of the galaxies used for training affects the behavior of the algorithm, and leads to a small but consistent and statistically significant bias. That bias exhibits itself in the form of cosmological-scale anisotropy in the distribution of basic galaxy morphology. Therefore, while DCNNs are powerful tools for annotating images of extended sources, the construction of training sets for galaxy morphology should take into consideration more aspects than the visual appearance of the object. In any case, catalogs created with deep neural networks that exhibit signs of cosmological anisotropy should be interpreted with the possibility of consistent bias.
