  Out-of-distribution (OOD) detection discerns OOD data where the predictor
cannot make valid predictions as in-distribution (ID) data, thereby increasing
the reliability of open-world classification. However, it is typically hard to
collect real out-of-distribution (OOD) data for training a predictor capable of
discerning ID and OOD patterns. This obstacle gives rise to data
generation-based learning methods, synthesizing OOD data via data generators
for predictor training without requiring any real OOD data. Related methods
typically pre-train a generator on ID data and adopt various selection
procedures to find those data likely to be the OOD cases. However, generated
data may still coincide with ID semantics, i.e., mistaken OOD generation
remains, confusing the predictor between ID and OOD data. To this end, we
suggest that generated data (with mistaken OOD generation) can be used to
devise an auxiliary OOD detection task to facilitate real OOD detection.
Specifically, we can ensure that learning from such an auxiliary task is
beneficial if the ID and the OOD parts have disjoint supports, with the help of
a well-designed training procedure for the predictor. Accordingly, we propose a
powerful data generation-based learning method named Auxiliary Task-based OOD
Learning (ATOL) that can relieve the mistaken OOD generation. We conduct
extensive experiments under various OOD detection setups, demonstrating the
effectiveness of our method against its advanced counterparts.
