  According to recent studies, commonly used computer vision datasets contain
about 4% of label errors. For example, the COCO dataset is known for its high
level of noise in data labels, which limits its use for training robust neural
deep architectures in a real-world scenario. To model such a noise, in this
paper we have proposed the homoscedastic aleatoric uncertainty estimation, and
present a series of novel loss functions to address the problem of image object
detection at scale. Specifically, the proposed functions are based on Bayesian
inference and we have incorporated them into the common community-adopted
object detection deep learning architecture RetinaNet. We have also shown that
modeling of homoscedastic aleatoric uncertainty using our novel functions
allows to increase the model interpretability and to improve the object
detection performance being evaluated on the COCO dataset.
