Transformation Based Deep Anomaly Detection in Astronomical Images
Universidad de Chile（智利大学）
Esteban Reyes, Pablo A. Estévez
备注：
8 pages, 6 figures, 4 tables. Accepted for publication in proceedings of the IEEE World Congress on Computational Intelligence (IEEE WCCI), Glasgow, UK, 19-24 July, 2020
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.
Video anomaly detection has gained significant attention due to the increasing requirements of automatic monitoring for surveillance videos. Especially, the prediction based approach is one of the most studied methods to detect anomalies by predicting frames that include abnormal events in the test set after learning with the normal frames of the training set. However, a lot of prediction networks are computationally expensive owing to the use of pre-trained optical flow networks, or fail to detect abnormal situations because of their strong generative ability to predict even the anomalies. To…
Tel Aviv University（特拉维夫大学）；Facebook AI Research（Facebook人工智能研究院）
Anomaly detection, the task of identifying unusual samples in data, often relies on a large set of training samples. In this work, we consider the setting of few-shot anomaly detection in images, where only a few images are given at training. We devise a hierarchical generative model that captures the multi-scale patch distribution of each training image. We further enhance the representation of our model by using image transformations and optimize scale-specific patch-discriminators to distinguish between real and fake patches of the image, as well as between different transformations applied…
Anomaly detection, finding patterns that substantially deviate from those seen previously, is one of the fundamental problems of artificial intelligence. Recently, classification-based methods were shown to achieve superior results on this task. In this work, we present a unifying view and propose an open-set method, GOAD, to relax current generalization assumptions. Furthermore, we extend the applicability of transformation-based methods to non-image data using random affine transformations. Our method is shown to obtain state-of-the-art accuracy and is applicable to broad data types. The str…
Anomaly-Aware Vision-Language Adapters for Zero-Shot Anomaly Detection
Dept. of Engineering for Innovation Medicine, University of Verona, Italy（创新医学工程系，威尼斯大学，意大利）；School of Computer Science and Engineering, Beihang University, China（计算机科学与工程学院，北航大学，中国）；Dept. of Computer Science, Reykjavik University, Iceland（计算机科学系，雷克雅未克大学，冰岛）
Netherlands Institute for Radio Astronomy (ASTRON)（荷兰射电天文研究所（ASTRON））
The new generation of radio telescopes, such as the Square Kilometer Array (SKA), requires dramatic advances in computer hardware and software, in order to process the large amounts of produced data efficiently. In this document, we explore a new approach to wide-field imaging. By generalizing the image reconstruction, which is performed by an inverse Fourier transform, to arbitrary transformations, we gain enormous new possibilities. In particular, we outline an approach that might allow to obtain a sky image of size P times Q in (optimal) O(PQ) time. This could be a step in the direction of …
University of Texas Rio Grande Valley（德克萨斯大学里奥格兰德河谷分校）；Centro Internacional Franco Argentino de Ciencias de la Información y de Sistemas（法阿国际信息科学与系统中心）；Instituto de Astronomía Teórica y Experimental - Observatorio Astronómico Córdoba（理论与实验天文学研究所-科尔多瓦天文台）；Duke University（杜克大学）
We present an algorithm implemented in the astroalign Python module for image registration in astronomy. Our module does not rely on WCS information and instead matches 3-point asterisms (triangles) on the images to find the most accurate linear transformation between the two. It is especially useful in the context of aligning images prior to stacking or performing difference image analysis. Astroalign can match images of different point-spread functions, seeing, and atmospheric conditions.
Fermi National Accelerator Laboratory（费米国家加速器实验室）；Argonne National Laboratory（阿贡国家实验室）；Kavli Institute for Cosmological Physics（卡夫利宇宙物理研究所）；University of Chicago（芝加哥大学）
In the era of big astronomical surveys, our ability to leverage artificial intelligence algorithms simultaneously for multiple datasets will open new avenues for scientific discovery. Unfortunately, simply training a deep neural network on images from one data domain often leads to very poor performance on any other dataset. Here we develop a Universal Domain Adaptation method DeepAstroUDA, capable of performing semi-supervised domain alignment that can be applied to datasets with different types of class overlap. Extra classes can be present in any of the two datasets, and the method can even…
Lawrence Berkeley National Laboratory（劳伦斯伯克利国家实验室）
Artificial intelligence methods show great promise in increasing the quality and speed of work with large astronomical datasets, but the high complexity of these methods leads to the extraction of dataset-specific, non-robust features. Therefore, such methods do not generalize well across multiple datasets. We present a universal domain adaptation method, \textit{DeepAstroUDA}, as an approach to overcome this challenge. This algorithm performs semi-supervised domain adaptation and can be applied to datasets with different data distributions and class overlaps. Non-overlapping classes can be pr…
Texas A&M University（德克萨斯农工大学）；Sidra Medicine（锡德拉医学中心）；Weill Cornell Medical College（威尔康奈尔医学院）
This paper presents a novel machine learning framework to consistently detect, localize and rate congenital cleft lip anomalies in human faces. The goal is to provide a universal, objective measure of facial differences and reconstructive surgical outcomes that matches human judgments. The proposed method employs the StyleGAN2 generative adversarial network with model adaptation to produce normalized transformations of cleft-affected faces in order to allow for subsequent measurement of deformity using a pixel-wise subtraction approach. The complete pipeline of the proposed framework consists …
Technical University of Munich（慕尼黑工业大学）；DeepC GmbH（DeepC有限公司）；Ludwig Maximilian University of Munich（慕尼黑路德维希·马克西米利安大学）；Johns Hopkins University（约翰斯·霍普金斯大学）
Medical imaging data suffers from the limited availability of annotation because annotating 3D medical data is a time-consuming and expensive task. Moreover, even if the annotation is available, supervised learning-based approaches suffer highly imbalanced data. Most of the scans during the screening are from normal subjects, but there are also large variations in abnormal cases. To address these issues, recently, unsupervised deep anomaly detection methods that train the model on large-sized normal scans and detect abnormal scans by calculating reconstruction error have been reported. In this…
