Recent studies on one-class classification have achieved a remarkable performance by employing the self-supervised classifier that predicts the type of pre-defined geometric transformations applied on in-class images. However, they cannot correctly identify in-class images as in-class at all when the input images have various viewpoints (e.g., translated or rotated images), because their classification-based in-class scores assume that in-class images always have a fixed viewpoint. Pointing out that humans can easily recognize such images having various viewpoints 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 remark that in-class images match better with the in-class transformations than out-of-class images do. We introduce a conformity score indicating 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.
|Number of pages||18|
|Publication status||Published - 2022 Sept|
Bibliographical noteFunding Information:
This work was supported by IITP grant funded by the Korea government MSIT (No. 2018-0-00584, SW starlab), the NRF grant funded by the MSIT (South Korea, No. 2020R1A2B5B03097210), and IITP grant funded by the MSIT (No. 2019-0-01906, AI graduate program of POSTECH).
© 2022 Elsevier Inc.
All Science Journal Classification (ASJC) codes
- Theoretical Computer Science
- Control and Systems Engineering
- Computer Science Applications
- Information Systems and Management
- Artificial Intelligence