Add to ORKGThough anomaly detection (AD) can be viewed as a classification problem (nominal vs. anomalous) it is usually treated in an unsupervised manner since one typically does not have access to, or it is infeasible to utilize, a dataset that sufficiently characterizes what it means to be "anomalous." In this paper we present results demonstrating that this intuition surprisingly seems not to extend to deep AD on images. For a recent AD benchmark on ImageNet, classifiers trained to discern between normal samples and just a few (64) random natural images are able to outperform the current state of the art in deep AD. Experimentally we discover that the multiscale structure of image data makes example anomalies exceptionally informative.Comment: 17 ...
Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals ...
Deep Learning models are easily disturbed by variations in the input images that were not observed d...
Deep anomaly detection methods learn representations that separate between normal and anomalous imag...
Anomaly detection has been considered under several extents of prior knowledge. Unsupervised methods...
Anomaly Detection (AD) is to identify samples that differ from training observations in some way. Th...
Selecting informative data points for expert feedback can significantly improve the performance of a...
We study anomaly detection for the case when the normal class consists of more than one object categ...
Detecting out-of-distribution examples is important for safety-critical machine learning application...
We present Eyecandies, a novel synthetic dataset for unsupervised anomaly detection and localization...
We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns abou...
Unsupervised out-of-distribution (U-OOD) detection has recently attracted much attention due its imp...
The detection and localization of anomalies is one important medical image analysis task. Most commo...
University of Minnesota M.S. thesis. May 2019. Major: Computer Science. Advisor: Edward McFowland II...
In this article, we propose using deep learning and transformer architectures combined with classica...
Novelty or anomaly detection is a challenging problem in many research disciplines without a general...
Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals ...
Deep Learning models are easily disturbed by variations in the input images that were not observed d...
Deep anomaly detection methods learn representations that separate between normal and anomalous imag...
Anomaly detection has been considered under several extents of prior knowledge. Unsupervised methods...
Anomaly Detection (AD) is to identify samples that differ from training observations in some way. Th...
Selecting informative data points for expert feedback can significantly improve the performance of a...
We study anomaly detection for the case when the normal class consists of more than one object categ...
Detecting out-of-distribution examples is important for safety-critical machine learning application...
We present Eyecandies, a novel synthetic dataset for unsupervised anomaly detection and localization...
We present a new algorithm for anomaly detection called Anomaly Awareness. The algorithm learns abou...
Unsupervised out-of-distribution (U-OOD) detection has recently attracted much attention due its imp...
The detection and localization of anomalies is one important medical image analysis task. Most commo...
University of Minnesota M.S. thesis. May 2019. Major: Computer Science. Advisor: Edward McFowland II...
In this article, we propose using deep learning and transformer architectures combined with classica...
Novelty or anomaly detection is a challenging problem in many research disciplines without a general...
Self-supervised learning (SSL) has emerged as a promising alternative to create supervisory signals ...
Deep Learning models are easily disturbed by variations in the input images that were not observed d...
Deep anomaly detection methods learn representations that separate between normal and anomalous imag...