Augment to Detect Anomalies with Continuous Labelling
- URL: http://arxiv.org/abs/2207.01112v1
- Date: Sun, 3 Jul 2022 20:11:51 GMT
- Title: Augment to Detect Anomalies with Continuous Labelling
- Authors: Vahid Reza Khazaie and Anthony Wong and Yalda Mohsenzadeh
- Abstract summary: Anomaly detection is to recognize samples that differ in some respect from the training observations.
Recent state-of-the-art deep learning-based anomaly detection methods suffer from high computational cost, complexity, unstable training procedures, and non-trivial implementation.
We leverage a simple learning procedure that trains a lightweight convolutional neural network, reaching state-of-the-art performance in anomaly detection.
- Score: 10.646747658653785
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: Anomaly detection is to recognize samples that differ in some respect from
the training observations. These samples which do not conform to the
distribution of normal data are called outliers or anomalies. In real-world
anomaly detection problems, the outliers are absent, not well defined, or have
a very limited number of instances. Recent state-of-the-art deep learning-based
anomaly detection methods suffer from high computational cost, complexity,
unstable training procedures, and non-trivial implementation, making them
difficult to deploy in real-world applications. To combat this problem, we
leverage a simple learning procedure that trains a lightweight convolutional
neural network, reaching state-of-the-art performance in anomaly detection. In
this paper, we propose to solve anomaly detection as a supervised regression
problem. We label normal and anomalous data using two separable distributions
of continuous values. To compensate for the unavailability of anomalous samples
during training time, we utilize straightforward image augmentation techniques
to create a distinct set of samples as anomalies. The distribution of the
augmented set is similar but slightly deviated from the normal data, whereas
real anomalies are expected to have an even further distribution. Therefore,
training a regressor on these augmented samples will result in more separable
distributions of labels for normal and real anomalous data points. Anomaly
detection experiments on image and video datasets show the superiority of the
proposed method over the state-of-the-art approaches.
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