Few-Shot One-Class Classification via Meta-Learning

• URL: http://arxiv.org/abs/2007.04146v2
• Date: Thu, 11 Feb 2021 18:52:13 GMT
• Title: Few-Shot One-Class Classification via Meta-Learning
• Authors: Ahmed Frikha, Denis Krompa{\ss}, Hans-Georg K\"opken and Volker Tresp
• Abstract summary: We study the intersection of few-shot learning and one-class classification (OCC) in a class-balanced test data set. This is done by explicitly optimizing steps with one-class minibatches to yield a performance increase on class-balanced test data.
• Score: 22.548520862073023
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Although few-shot learning and one-class classification (OCC), i.e., learning a binary classifier with data from only one class, have been separately well studied, their intersection remains rather unexplored. Our work addresses the few-shot OCC problem and presents a method to modify the episodic data sampling strategy of the model-agnostic meta-learning (MAML) algorithm to learn a model initialization particularly suited for learning few-shot OCC tasks. This is done by explicitly optimizing for an initialization which only requires few gradient steps with one-class minibatches to yield a performance increase on class-balanced test data. We provide a theoretical analysis that explains why our approach works in the few-shot OCC scenario, while other meta-learning algorithms fail, including the unmodified MAML. Our experiments on eight datasets from the image and time-series domains show that our method leads to better results than classical OCC and few-shot classification approaches, and demonstrate the ability to learn unseen tasks from only few normal class samples. Moreover, we successfully train anomaly detectors for a real-world application on sensor readings recorded during industrial manufacturing of workpieces with a CNC milling machine, by using few normal examples. Finally, we empirically demonstrate that the proposed data sampling technique increases the performance of more recent meta-learning algorithms in few-shot OCC and yields state-of-the-art results in this problem setting.

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