Published: May 7, 2024

Deep learning has increasingly been employed to identify minerals. However, deep can only be used minerals in the distribution of training set, while any mineral outside spectrum set is inevitably categorized erroneously within a predetermined class from set. To solve this problem, study introduces approach that amalgamates One-Class Support Vector Machines (OCSVM) with ResNet architecture for out-of-distribution detection. Initially, undergoes using comprising well-defined Subsequently, earlier layers trained are extract discriminative features under consideration. These extracted then become input OCSVM. When OCSVM discerns set's distribution, it triggers subsequent ResNet, facilitating accurate classification into one predefined categories encompassing known In event identifies out unequivocally as an unclassified or 'unknown' mineral. Empirical results substantiate method's capability concurrently maintaining commendably high accuracy rate 36 in-distribution

Language: Английский