  User activities generate a significant number of poor-quality or irrelevant
images and data vectors that cannot be processed in the main data processing
pipeline or included in the training dataset. Such samples can be found with
manual analysis by an expert or with anomalous detection algorithms. There are
several formal definitions for the anomaly samples. For neural networks, the
anomalous is usually defined as out-of-distribution samples. This work proposes
methods for supervised and semi-supervised detection of out-of-distribution
samples in image datasets. Our approach extends a typical neural network that
solves the image classification problem. Thus, one neural network after
extension can solve image classification and anomalous detection problems
simultaneously. Proposed methods are based on the center loss and its effect on
a deep feature distribution in a last hidden layer of the neural network. This
paper provides an analysis of the proposed methods for the LeNet and
EfficientNet-B0 on the MNIST and ImageNet-30 datasets.
