116 research outputs found
GAN Ensemble for Anomaly Detection
When formulated as an unsupervised learning problem, anomaly detection often
requires a model to learn the distribution of normal data. Previous works apply
Generative Adversarial Networks (GANs) to anomaly detection tasks and show good
performances from these models. Motivated by the observation that GAN ensembles
often outperform single GANs in generation tasks, we propose to construct GAN
ensembles for anomaly detection. In the proposed method, a group of generators
and a group of discriminators are trained together, so every generator gets
feedback from multiple discriminators, and vice versa. Compared to a single
GAN, a GAN ensemble can better model the distribution of normal data and thus
better detect anomalies. Our theoretical analysis of GANs and GAN ensembles
explains the role of a GAN discriminator in anomaly detection. In the empirical
study, we evaluate ensembles constructed from four types of base models, and
the results show that these ensembles clearly outperform single models in a
series of tasks of anomaly detection.Comment: 8 pages, 6 figures. To appear in Proceedings 35th AAAI Conference on
Artificial Intelligence (AAAI 21
Representation Learning in Anomaly Detection: Successes, Limits and a Grand Challenge
In this perspective paper, we argue that the dominant paradigm in anomaly
detection cannot scale indefinitely and will eventually hit fundamental limits.
This is due to the a no free lunch principle for anomaly detection. These
limitations can be overcome when there are strong tasks priors, as is the case
for many industrial tasks. When such priors do not exists, the task is much
harder for anomaly detection. We pose two such tasks as grand challenges for
anomaly detection: i) scientific discovery by anomaly detection ii) a
"mini-grand" challenge of detecting the most anomalous image in the ImageNet
dataset. We believe new anomaly detection tools and ideas would need to be
developed to overcome these challenges.Comment: Keynote talk at the Visual Anomaly and Novelty Detection Workshop,
CVPR'2
Few-Shot Anomaly Detection for Polyp Frames from Colonoscopy
Anomaly detection methods generally target the learning of a normal image
distribution (i.e., inliers showing healthy cases) and during testing, samples
relatively far from the learned distribution are classified as anomalies (i.e.,
outliers showing disease cases). These approaches tend to be sensitive to
outliers that lie relatively close to inliers (e.g., a colonoscopy image with a
small polyp). In this paper, we address the inappropriate sensitivity to
outliers by also learning from inliers. We propose a new few-shot anomaly
detection method based on an encoder trained to maximise the mutual information
between feature embeddings and normal images, followed by a few-shot score
inference network, trained with a large set of inliers and a substantially
smaller set of outliers. We evaluate our proposed method on the clinical
problem of detecting frames containing polyps from colonoscopy video sequences,
where the training set has 13350 normal images (i.e., without polyps) and less
than 100 abnormal images (i.e., with polyps). The results of our proposed model
on this data set reveal a state-of-the-art detection result, while the
performance based on different number of anomaly samples is relatively stable
after approximately 40 abnormal training images.Comment: Accept at MICCAI 202
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