In one-class-learning tasks, only the normal case can be modeled with data, whereas the variation of all possible anomalies is too large to be described sufficiently 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 normal cases, 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 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 which enforces diversity across hypotheses. This consistency-based anomaly detection (ConAD) framework allows the reliable identification of outof- 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%. Anomaly detection classifies a sample as normal or abnormal. In many applications, however, it must be treated as a one-class-learning problem, since the abnormal class cannot be defined sufficiently by samples. Samples of the abnormal class can be extremely rare, or they do not cover the full space of possible anomalies. For instance, in an autonomous driving system, we may have a test case with a bear or a kangaroo on the road. For defect detection in manufacturing, new, unknown production anomalies due to critical changes in the production environment can appear. In medical data analysis, there can be unknown deviations from the healthy state. In all these cases, the well-studied discriminative models, where decision boundaries of classifiers are learned from training samples of all classes, cannot be applied. The decision boundary learning of discriminative models will be dominated by the normal class, which will negatively influence the classification performance.Anomaly detection as one-class learning is typically approached by generative, reconstruction-based methods BID30 . They approximate the input distribution of the normal cases by parametric models, which allow them to reconstruct input samples from this distribution. At test time, the data log-likelihood serves as an anomaly-score. In the case of high-dimensional inputs, such as images, learning a representative distribution model of the normal class is hard and requires many samples.Typically, an autoencoder-based approach such as the variational autoencoder BID21 BID13 ) is used. Autoencoders tend to produce blurry reconstructions, since they regress the conditional mean, and cannot model multi-modal distributions; see FIG0 for an example on a Metal Anomaly dataset. Due to multiple modes in the actual distribution, the approximation with the mean predicts high probabilities in areas not supported by samples. The blurry reconstructions in FIG0 should have a low probability and be classified as anomalies, but they have the highest likelihood under the learned autoencoder.Multiple-hypotheses networks could give the model more expressive power BID23 , BID5 , BID11 , BID2 . In conjunction with autoencoders, the multiple hypotheses can be realized with a multi-headed decoder. Concretely, each network head may predict a Gaussian density estimate. gives the network more expressive power with a multi-headed decoder (also known as multiple-hypotheses networks). The resulting anomaly scores are hence much clearer in our framework ConAD. In this work, we propose to employ multiple-hypotheses networks for learning data distributions for anomaly detection tasks. Hypotheses are meant to form clusters in the data space and can easily capture model uncertainty not encoded by the latent code. multiple-hypotheses networks can provide a more fine-grained description of the data distribution and therefore enable also a more fine-grained anomaly detection. Furthermore, to reduce support of artificial data modes by hypotheses learning, we propose using a discriminator D as a critic. The combination of multiple-hypotheses learning with D aims to retain the consistency of estimated data modes w.r.t. the real data distribution. Further, D encourage diversity across hypotheses with hypotheses discrimination. Our framework allows the model to identify out-of-distribution samples reliably.For the anomaly detection task on CIFAR-10, our proposed model results in 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%.
We propose an anomaly-detection approach that combines modeling the foreground class via multiple local densities with adversarial training. The paper proposes a technique to make generative models more robust by making them consistent with the local density.