<Record><identifier xmlns="http://purl.org/dc/elements/1.1/">URN:NBN:SI:DOC-35GSO9WU</identifier><date>2022</date><creator>Božič, Jakob</creator><creator>Dobrevski, Matej</creator><creator>Skočaj, Danijel</creator><relation>documents/doc/3/URN_NBN_SI_doc-35GSO9WU_001.pdf</relation><relation>documents/doc/3/URN_NBN_SI_doc-35GSO9WU_001.txt</relation><format format_type="type">article</format><format format_type="extent">Str. 385-389</format><identifier identifier_type="COBISSID_HOST">132811523</identifier><identifier identifier_type="ISSN">2591-0442</identifier><identifier identifier_type="URN">URN:NBN:SI:doc-35GSO9WU</identifier><language>eng</language><publisher publisher_location="Ljubljana">Fakulteta za elektrotehniko</publisher><publisher publisher_location="Ljubljana">Slovenska sekcija IEEE</publisher><source>Zbornik mednarodne Elektrotehniške in računalniške konference</source><rights>InC</rights><subject language_type_id="eng">anomaly detection</subject><subject language_type_id="eng">convolutional neural networks</subject><subject language_type_id="eng">deep learning</subject><subject language_type_id="slv">detekcija anomalij</subject><subject language_type_id="slv">detekcija površinskih napak</subject><subject language_type_id="eng">domain adaptation</subject><subject language_type_id="slv">domenska adaptacija</subject><subject language_type_id="slv">globoko učenje</subject><subject language_type_id="slv">konvolucijske nevronske mreže</subject><subject language_type_id="slv">nenadzorovano učenje</subject><subject language_type_id="eng">surface defect detection</subject><subject language_type_id="eng">unsupervised learning</subject><subject language_type_id="eng">visual inspection</subject><subject language_type_id="slv">vizualni pregled</subject><title>Robustness of surface anomaly detection methods to domain shift</title></Record>