One-class Classification Robust to Geometric Transformation

Hyunjun Ju, Dongha Lee, SeongKu Kang, Hwanjo Yu

Keywords: one-class classification, image classification, object classification, self-supervised learning, geometric robustness

Abstract: Recent studies on one-class classification have achieved a remarkable performance, by employing the self-supervised classifier that predicts the geometric transformation applied to in-class images. However, they cannot identify in-class images at all when the input images are geometrically-transformed (e.g., rotated images), because their classification-based in-class scores assume that input images always have a fixed viewpoint, as similar to the images used for training. Pointing out that humans can easily recognize such transformed images as the same class, in this work, we aim to propose a one-class classifier robust to geometrically-transformed inputs, named as GROC. To this end, we introduce a conformity score which indicates how strongly an input image agrees with one of the predefined in-class transformations, then utilize the conformity score with our proposed agreement measures for one-class classification. Our extensive experiments demonstrate that GROC is able to accurately distinguish in-class images from out-of-class images regardless of whether the inputs are geometrically-transformed or not, whereas the existing methods fail.

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One-sentence Summary: This paper proposes a one-class classification method robust to geometric transformations, which effectively addresses the challenge that in-class images cannot be correctly distinguished from out-of-class images when they have various viewpoints.

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