Abstract
Advances in deep neural networks (DNNs) have led to impressive results and in recent years many works have exploited DNNs for anomaly detection. Among others, generative/reconstruction model-based methods have been frequently used for anomaly detection because they do not require any labels for training. The anomaly detection performance of these methods, however, varies a lot, due to the change of the intra-class variance and the difference in complexity of input samples. In addition, most previous state-of-the-art works on anomaly detection have empirically adjusted several hyperparameters to heighten their performance of anomaly detection. These sorts of procedures are known to be impractical and create obstacles in real world anomaly detection. To solve these problems, we propose a hybrid discriminator with a correlative autoencoder for anomaly detection. In the proposed framework, the discriminator implicitly estimates the conditional probability density function and the autoencoder has improved ability to control the reconstruction error. We provide theoretical foundation of our method and verify it through various experiments. We also confirm practical benefits of our interpretation of the conditional expectation and the proposed framework by comparing our results with other state-of-the-art methods.
Highlights
The recent explosive growth in machine learning and big data applications has increased the need for techniques that can detect anomalies
In order to solve the performance degradation problem due to the change of the intra-class variance and the difference in input complexity, we propose a framework for anomaly detection which is capable of conditionally discriminating and selectively controlling the reconstruction error by using a hybrid discriminator with a correlative autoencoder
We focus on the anomaly detection problem which uses only in-distribution samples without any labels during training, and verify the proposed method through experiments for one-class classification (OCC), OOD detection, and multiple class anomaly detection (MCAD)
Summary
The recent explosive growth in machine learning and big data applications has increased the need for techniques that can detect anomalies. No one has reported experimental results under this setting but we have found that increasing the number of classes for in-distribution samples dramatically decreases the anomaly detection performance, especially in previous generative/reconstruction model-based approaches. Instances [1], [5], [19] have been found where the probability density likelihood of untrained OOD samples is higher than that of trained in-distribution samples, which are inconsistent with the idea [17], [18] that generative models can be used for anomaly detection. In order to solve the performance degradation problem due to the change of the intra-class variance and the difference in input complexity, we propose a framework for anomaly detection which is capable of conditionally discriminating and selectively controlling the reconstruction error by using a hybrid discriminator with a correlative autoencoder.
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