Multi-label out-of-distribution detection via evidential learning

Eduardo Aguilar, Bogdan Raducanu, Petia Radeva

Abstract:

A crucial requirement for machine learning algorithms is not
only to perform well, but also to show robustness and adaptability when
encountering novel scenarios. One way to achieve these characteristics
is to endow the deep learning models with the ability to detect out-ofdistribution (OOD) data, i.e. data that belong to distributions different
from the one used during their training. It is even a more complicated
situation, when these data usually are multi-label. In this paper, we propose an approach based on evidential deep learning in order to meet
these challenges applied to visual recognition problems. More concretely,
we designed a CNN architecture that uses a Beta Evidential Neural Network to compute both the likelihood and the predictive uncertainty of
the samples. Based on these results, we propose afterwards two new
uncertainty-based scores for OOD data detection: (i) OOD - score Max,
based on the maximum evidence; and (ii) OOD score - Sum, which considers the evidence from all outputs. Extensive experiments have been
carried out to validate the proposed approach using three widely-used
datasets: PASCAL-VOC, MS-COCO and NUS-WIDE, demonstrating its
outperformance over several State-of-the-Art methods.