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.

翻译：离群点检测（OD）在众多领域有着广泛应用，并已发展出丰富的技术体系。基于深度神经网络的离群点检测（DOD）方法，得益于深度学习的诸多进展，近年来受到广泛关注。本文聚焦于无监督DOD中一个关键但研究不足的挑战：有效的超参数（HP）调优与模型选择。尽管已有研究指出OD模型对超参数具有敏感性，但对于拥有大量超参数的现代DOD模型而言，这一问题变得尤为关键。我们提出HYPER方法用于调优DOD模型，旨在解决两个根本性挑战：（1）在无监督情况下进行验证（由于缺乏标记的异常样本），以及（2）高效搜索超参数/模型空间（源于超参数数量的指数级增长）。其核心思想是设计并训练一种新颖的超网络（HN），该网络能够将超参数映射至主DOD模型的最优权重。由此，HYPER利用单个超网络即可动态生成多种DOD模型（对应不同超参数）的权重，从而显著提升效率。此外，该方法通过对带标签的历史OD任务进行元学习，训练一个代理验证函数，该函数同样通过我们提出的超网络进行高效训练。在35个OD任务上的大量实验表明，HYPER相较于8个基线方法取得了优越的性能，并具有显著的效率优势。