Anomaly Detection With Multiple-Hypotheses Predictions
This addresses the challenge of detecting anomalies in domains like images where generative models are inefficient, offering a novel method for improved performance in specific applications.
The paper tackles the problem of anomaly detection in one-class-learning tasks by proposing a multi-hypotheses autoencoder with a discriminator to efficiently learn the normal data distribution and identify out-of-distribution samples, resulting in up to 3.9% improvement on CIFAR-10 and error reduction from 6.8% to 1.5% on a real task.
In one-class-learning tasks, only the normal case (foreground) can be modeled with data, whereas the variation of all possible anomalies is too erratic to be described by samples. Thus, due to the lack of representative data, the wide-spread discriminative approaches cannot cover such learning tasks, and rather generative models, which attempt to learn the input density of the foreground, are used. However, generative models suffer from a large input dimensionality (as in images) and are typically inefficient learners. We propose to learn the data distribution of the foreground more efficiently with a multi-hypotheses autoencoder. Moreover, the model is criticized by a discriminator, which prevents artificial data modes not supported by data, and enforces diversity across hypotheses. Our multiple-hypothesesbased anomaly detection framework allows the reliable identification of out-of-distribution samples. For anomaly detection on CIFAR-10, it yields up to 3.9% points improvement over previously reported results. On a real anomaly detection task, the approach reduces the error of the baseline models from 6.8% to 1.5%.