  Handling entirely unknown data is a challenge for any deployed classifier.
Classification models are typically trained on a static pre-defined dataset and
are kept in the dark for the open unassigned feature space. As a result, they
struggle to deal with out-of-distribution data during inference. Addressing
this task on the class-level is termed open-set recognition (OSR). However,
most OSR methods are inherently limited, as they train closed-set classifiers
and only adapt the downstream predictions to OSR. This work presents LORD, a
framework to Leverage Open-set Recognition by exploiting unknown Data. LORD
explicitly models open space during classifier training and provides a
systematic evaluation for such approaches. We identify three model-agnostic
training strategies that exploit background data and applied them to
well-established classifiers. Due to LORD's extensive evaluation protocol, we
consistently demonstrate improved recognition of unknown data. The benchmarks
facilitate in-depth analysis across various requirement levels. To mitigate
dependency on extensive and costly background datasets, we explore mixup as an
off-the-shelf data generation technique. Our experiments highlight mixup's
effectiveness as a substitute for background datasets. Lightweight constraints
on mixup synthesis further improve OSR performance.
