Consistency-based anomaly detection with adaptive multiple-hypotheses predictions

In out-of-distribution classification tasks, only some classes - the normal cases - can be modeled with data, whereas the variation of all possible anomalies is too large to be described sufficiently by samples. Thus, the widespread discriminative approaches cannot cover such learning tasks and rather generative models, which attempt to learn the input density of the ordinary cases, are used. However, generative models suffer under a large input dimensionality (as in images) and are typically inefficient learners. Motivated by the Local-Outlier-Factor (LOF) method, in this work, we propose to allow the network to directly estimate the local density functions since, for the detection of outliers, the local neighborhood is more important than the global one. At the same time, we retain consistency in the sense that the model must not support areas of the input space that are not covered by samples. Our method allows the model to identify out-of-distribution samples reliably. For the anomaly detection task on CIFAR-10, our ConAD model results in up to 5% points improvement over previously reported results. Anomaly detection tasks belong to the category of one-class-learning and are crucial in many applications, where a fixed set of classes cannot be defined, for instance, because a subset of classes is extremely rare or some classes are unknown at training time. For example, there might be a bear crossing the street as part of validation scenarios for automatic cars, unknown production anomalies due to critical change of the production environment, or unknown deviations from the healthy state in medical data.