50 research outputs found
Learning Representations for Novelty and Anomaly Detection
The problem of novelty or anomaly detection refers to the ability to automatically
identify data samples that differ from a notion of normality. Techniques
that address this problem are necessary in many applications, like in medical
diagnosis, autonomous driving, fraud detection, or cyber-attack detection, just to
mention a few. The problem is inherently challenging because of the openness of
the space of distributions that characterize novelty or outlier data points. This is
often matched with the inability to adequately represent such distributions due
to the lack of representative data.
In this dissertation we address the challenge above by making several contributions.
(a)We introduce an unsupervised framework for novelty detection,
which is based on deep learning techniques, and which does not require labeled
data representing the distribution of outliers. (b) The framework is general and
based on first principles by detecting anomalies via computing their probabilities
according to the distribution representing normality. (c) The framework can
handle high-dimensional data such as images, by performing a non-linear dimensionality
reduction of the input space into an isometric lower-dimensional space,
leading to a computationally efficient method. (d) The framework is guarded
from the potential inclusion of distributions of outliers into the distribution of
normality by favoring that only inlier data can be well represented by the model.
(e) The methods are evaluated extensively on multiple computer vision benchmark
datasets, where it is shown that they compare favorably with the state of
the art
Rethinking Assumptions in Deep Anomaly Detection
Though 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 pages; accepted at the ICML 2021 Workshop on Uncertainty &
Robustness in Deep Learnin