2024
A Survey of Unsupervised Learning-Based Out-of-Distribution Detection
Hyeongseob Jo, Seunggi Park, Sung In Cho
IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) 2024
Out-of-distribution (OOD) detection is the task of distinguishing abnormal data that lies outside the bounds of the training dataset’s distribution. OOD detection plays a vital role in various applications of machine learning and deep learning, including intrusion detection in cybersecurity, diagnostics in medical data, and defect classification in manufacturing processes. While models for OOD detection are typically trained using supervised learning, this approach requires significant cost and effort such as collection and labeling of OOD data. To address this issue, unsupervised learning-based methods have been proposed, which can overcome the drawbacks of supervised approaches. In this paper, we introduce generative model-based OOD methods and self-supervised OOD detection methods within the realm of unsupervised learning.
A Survey of Unsupervised Learning-Based Out-of-Distribution Detection
Hyeongseob Jo, Seunggi Park, Sung In Cho
IEEE International Conference on Consumer Electronics-Asia (ICCE-Asia) 2024
Out-of-distribution (OOD) detection is the task of distinguishing abnormal data that lies outside the bounds of the training dataset’s distribution. OOD detection plays a vital role in various applications of machine learning and deep learning, including intrusion detection in cybersecurity, diagnostics in medical data, and defect classification in manufacturing processes. While models for OOD detection are typically trained using supervised learning, this approach requires significant cost and effort such as collection and labeling of OOD data. To address this issue, unsupervised learning-based methods have been proposed, which can overcome the drawbacks of supervised approaches. In this paper, we introduce generative model-based OOD methods and self-supervised OOD detection methods within the realm of unsupervised learning.