Unsupervised reconstructive and discriminative methods for surface anomaly detection and localisationZavrtanik, Vitjan (Avtor) Skočaj, Danijel (Mentor) anomaly detectionunsupervised learningsurface anomaly detectionThis thesis deals with the task of unsupervised surface anomaly detection and localization. Through an overview of the surface anomaly detection field, it describes the main paradigms of the current surface anomaly detection methods, analyses the field's most recent best performing methods and points out their shortcomings. As the main part of the thesis, several methods addressing the issues of the reconstructive and the discriminative anomaly detection paradigms are proposed. The three proposed surface anomaly detection methods represent the main contributions to science of the doctoral work. The RIAD method belongs to the reconstructive paradigm. It reformulates image reconstruction as an iterative inpainting process, preventing the reconstruction of anomalies, making them detectable. The DRAEM method is a discriminative anomaly detection method that is trained using simulated anomalies. It proposes a reconstructive and discriminative framework that removes anomalies from the image and then accurately localizes them based on the difference between the input image and the anomaly-free reconstruction. The DSR method simulates anomalies directly in the discrete latent space, which improves the diversity of simulated anomalies and enables the accurate detection of real anomalies. As an additional contribution, the use of the proposed discriminative methods on other data modalities has been explored, demonstrating the significant adaptability of the proposed architectures.20252025-10-13 10:05:08Doktorsko delo/naloga175005VisID: 35305COBISS_ID: 254035715sl