  Vision systems trained in closed-world scenarios fail when presented with new
environmental conditions, new data distributions, and novel classes at
deployment time. How to move towards open-world learning is a long-standing
research question. The existing solutions mainly focus on specific aspects of
the problem (single domain Open-Set, multi-domain Closed-Set), or propose
complex strategies which combine several losses and manually tuned
hyperparameters. In this work, we tackle multi-source Open-Set domain
adaptation by introducing HyMOS: a straightforward model that exploits the
power of contrastive learning and the properties of its hyperspherical feature
space to correctly predict known labels on the target, while rejecting samples
belonging to any unknown class. HyMOS includes style transfer among the
instance transformations of contrastive learning to get domain invariance while
avoiding the risk of negative-transfer. A self-paced threshold is defined on
the basis of the observed data distribution and updates online during training,
allowing to handle the known-unknown separation. We validate our method over
three challenging datasets. The obtained results show that HyMOS outperforms
several competitors, defining the new state-of-the-art. Our code is available
at https://github.com/silvia1993/HyMOS.
