Deep learning approaches to anomaly detection (AD) have recently improved the state of
the art in detection performance on complex data sets, such as large collections of images or …

Anomaly Detection (AD) in images is a fundamental computer vision problem and refers to
identifying images and/or image substructures that deviate significantly from the norm …

Abstract Out-of-distribution (OOD) detection is important for machine learning models
deployed in the wild. Recent methods use auxiliary outlier data to regularize the model for …

Anomaly localization is an important problem in computer vision which involves localizing
anomalous regions within images with applications in industrial inspection, surveillance …

The recently introduced introspective variational autoencoder (IntroVAE) exhibits
outstanding image generations, and allows for amortized inference using an image encoder …

The autoencoder (AE) is a fundamental deep learning approach to anomaly detection. AEs
are trained on the assumption that abnormal inputs will produce higher reconstruction errors …

We propose a new method for novelty detection that can tolerate high corruption of the
training points, whereas previous works assumed either no or very low corruption. Our …

Natural disasters, such as floods, can cause significant damage to both the environment and
human life. Rapid and accurate identification of affected areas is crucial for effective disaster …

Deep neural networks often predict samples with high confidence even when they come
from unseen classes and should instead be flagged for expert evaluation. Current novelty …

Deep anomaly detection has recently seen significant developments to provide robust and
efficient classifiers using only a few anomalous samples. Many of those models consist in a …