4 research outputs found
Exploring Deep Anomaly Detection Methods Based on Capsule Net
In this paper, we develop and explore deep anomaly detection techniques based
on the capsule network (CapsNet) for image data. Being able to encoding
intrinsic spatial relationship between parts and a whole, CapsNet has been
applied as both a classifier and deep autoencoder. This inspires us to design a
prediction-probability-based and a reconstruction-error-based normality score
functions for evaluating the "outlierness" of unseen images. Our results on
three datasets demonstrate that the prediction-probability-based method
performs consistently well, while the reconstruction-error-based approach is
relatively sensitive to the similarity between labeled and unlabeled images.
Furthermore, both of the CapsNet-based methods outperform the principled
benchmark methods in many cases.Comment: Presented in the "ICML 2019 Workshop on Uncertainty & Robustness in
Deep Learning", June 14, Long Beach, California, US
Multiresolution Knowledge Distillation for Anomaly Detection
Unsupervised representation learning has proved to be a critical component of
anomaly detection/localization in images. The challenges to learn such a
representation are two-fold. Firstly, the sample size is not often large enough
to learn a rich generalizable representation through conventional techniques.
Secondly, while only normal samples are available at training, the learned
features should be discriminative of normal and anomalous samples. Here, we
propose to use the "distillation" of features at various layers of an expert
network, pre-trained on ImageNet, into a simpler cloner network to tackle both
issues. We detect and localize anomalies using the discrepancy between the
expert and cloner networks' intermediate activation values given the input
data. We show that considering multiple intermediate hints in distillation
leads to better exploiting the expert's knowledge and more distinctive
discrepancy compared to solely utilizing the last layer activation values.
Notably, previous methods either fail in precise anomaly localization or need
expensive region-based training. In contrast, with no need for any special or
intensive training procedure, we incorporate interpretability algorithms in our
novel framework for the localization of anomalous regions. Despite the striking
contrast between some test datasets and ImageNet, we achieve competitive or
significantly superior results compared to the SOTA methods on MNIST, F-MNIST,
CIFAR-10, MVTecAD, Retinal-OCT, and two Medical datasets on both anomaly
detection and localization
Unsupervised Anomaly Detection with Multi-scale Interpolated Gaussian Descriptors
Current unsupervised anomaly detection and pixel-wise anomaly localisation
systems are commonly formulated as one-class classifiers that depend on an
effective estimation of the distribution of normal images and robust criteria
to identify anomalies. However, the distribution of normal images estimated by
current systems tends to be unstable for classes of normal images that are
under-represented in the training set, and the anomaly identification criteria
commonly explored in the field does not work well for multi-scale structural
and non-structural anomalies. In this paper, we introduce a new unsupervised
anomaly detection and localisation method designed to address these two issues.
More specifically, we introduce a normal image distribution estimation method
that is robust to under-represented classes of normal images -- this method is
based on adversarially interpolated descriptors from training images and a
Gaussian classifier. We also propose a new anomaly identification criterion
that can accurately detect and localise multi-scale structural and
non-structural anomalies. In extensive experiments on MNIST, Fashion MNIST,
CIFAR10, MVTec AD and two medical datasets, our approach shows better results
than the current state of the art in the standard experimental setup for
unsupervised anomaly detection and localisation. Code is available at
https://github.com/tianyu0207/IGD.Comment: Under Revie
Puzzle-AE: Novelty Detection in Images through Solving Puzzles
Autoencoder, as an essential part of many anomaly detection methods, is
lacking flexibility on normal data in complex datasets. U-Net is proved to be
effective for this purpose but overfits on the training data if trained by just
using reconstruction error similar to other AE-based frameworks.
Puzzle-solving, as a pretext task of self-supervised learning (SSL) methods,
has earlier proved its ability in learning semantically meaningful features. We
show that training U-Nets based on this task is an effective remedy that
prevents overfitting and facilitates learning beyond pixel-level features.
Shortcut solutions, however, are a big challenge in SSL tasks, including jigsaw
puzzles. We propose adversarial robust training as an effective automatic
shortcut removal. We achieve competitive or superior results compared to the
State of the Art (SOTA) anomaly detection methods on various toy and real-world
datasets. Unlike many competitors, the proposed framework is stable, fast,
data-efficient, and does not require unprincipled early stopping