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Outlier detection is an important data mining task with numerous practicalapplications such as intrusion detection, credit card fraud detection, andvideo surveillance. However, given a specific complicated task with big data,the process of building a powerful deep learning based system for outlierdetection still highly relies on human expertise and laboring trials. AlthoughNeural Architecture Search (NAS) has shown its promise in discovering effectivedeep architectures in various domains, such as image classification, objectdetection, and semantic segmentation, contemporary NAS methods are not suitablefor outlier detection due to the lack of intrinsic search space, unstablesearch process, and low sample efficiency. To bridge the gap, in this paper, wepropose AutoOD, an automated outlier detection framework, which aims to searchfor an optimal neural network model within a predefined search space.Specifically, we firstly design a curiosity-guided search strategy to overcomethe curse of local optimality. A controller, which acts as a search agent, isencouraged to take actions to maximize the information gain about thecontroller's internal belief. We further introduce an experience replaymechanism based on self-imitation learning to improve the sample efficiency.Experimental results on various real-world benchmark datasets demonstrate thatthe deep model identified by AutoOD achieves the best performance, comparingwith existing handcrafted models and traditional search methods.

AutoOD: Automated Outlier Detection via Curiosity-guided Search and Self-imitation Learning