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Outlier detection (OD) finds many applications with a rich literature of
numerous techniques. Deep neural network based OD (DOD) has seen a recent surge
of attention thanks to the many advances in deep learning. In this paper, we
consider a critical-yet-understudied challenge with unsupervised DOD, that is,
effective hyperparameter (HP) tuning/model selection. While several prior work
report the sensitivity of OD models to HPs, it becomes ever so critical for the
modern DOD models that exhibit a long list of HPs. We introduce HYPER for
tuning DOD models, tackling two fundamental challenges: (1) validation without
supervision (due to lack of labeled anomalies), and (2) efficient search of the
HP/model space (due to exponential growth in the number of HPs). A key idea is
to design and train a novel hypernetwork (HN) that maps HPs onto optimal
weights of the main DOD model. In turn, HYPER capitalizes on a single HN that
can dynamically generate weights for many DOD models (corresponding to varying
HPs), which offers significant speed-up. In addition, it employs meta-learning
on historical OD tasks with labels to train a proxy validation function,
likewise trained with our proposed HN efficiently. Extensive experiments on 35
OD tasks show that HYPER achieves high performance against 8 baselines with
significant efficiency gains.
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