论文标题
基于转化的天文图像中的深度异常检测
Transformation Based Deep Anomaly Detection in Astronomical Images
论文作者
论文摘要
在这项工作中，我们提出了基于几何变换模型的几种增强功能，以用于图像中的异常检测（Geotranform）。该模型假设异常类是未知的，并且只有较低的样本可供训练。我们引入了新的基于滤波器的转换，可用于检测天文图像中的异常情况，这些转换突出了伪影特性，使它们更容易与真实对象区分。此外，我们提出了一种转换选择策略，该策略使我们能够找到一对无法区分的转换对。这会改善接收器操作特征曲线（AUROC）和准确性性能以及降低维度的情况。这些模型在高节奏瞬态调查（HITS）和Zwicky瞬态设施（ZTF）数据集的天文图像上进行了测试。最佳模型的命中率平均为99.20％，ZTF的平均AUROC为91.39％。对原始的地理转化算法和基线方法（例如一级支持向量机以及基于深度学习的方法）的改进在统计学和实践上都是显着的。
In this work, we propose several enhancements to a geometric transformation based model for anomaly detection in images (GeoTranform). The model assumes that the anomaly class is unknown and that only inlier samples are available for training. We introduce new filter based transformations useful for detecting anomalies in astronomical images, that highlight artifact properties to make them more easily distinguishable from real objects. In addition, we propose a transformation selection strategy that allows us to find indistinguishable pairs of transformations. This results in an improvement of the area under the Receiver Operating Characteristic curve (AUROC) and accuracy performance, as well as in a dimensionality reduction. The models were tested on astronomical images from the High Cadence Transient Survey (HiTS) and Zwicky Transient Facility (ZTF) datasets. The best models obtained an average AUROC of 99.20% for HiTS and 91.39% for ZTF. The improvement over the original GeoTransform algorithm and baseline methods such as One-Class Support Vector Machine, and deep learning based methods is significant both statistically and in practice.
