Version 1
: Received: 6 May 2024 / Approved: 6 May 2024 / Online: 7 May 2024 (02:24:40 CEST)
How to cite:
Ji, X.; Liang, K.; Yang, Y.; Yang, M.; He, M.; Zhang, Z.; Zeng, S.; Wang, Y. Mineral Identification based on Out-Of-Distribution Detection. Preprints2024, 2024050312. https://doi.org/10.20944/preprints202405.0312.v1
Ji, X.; Liang, K.; Yang, Y.; Yang, M.; He, M.; Zhang, Z.; Zeng, S.; Wang, Y. Mineral Identification based on Out-Of-Distribution Detection. Preprints 2024, 2024050312. https://doi.org/10.20944/preprints202405.0312.v1
Ji, X.; Liang, K.; Yang, Y.; Yang, M.; He, M.; Zhang, Z.; Zeng, S.; Wang, Y. Mineral Identification based on Out-Of-Distribution Detection. Preprints2024, 2024050312. https://doi.org/10.20944/preprints202405.0312.v1
APA Style
Ji, X., Liang, K., Yang, Y., Yang, M., He, M., Zhang, Z., Zeng, S., & Wang, Y. (2024). Mineral Identification based on Out-Of-Distribution Detection. Preprints. https://doi.org/10.20944/preprints202405.0312.v1
Chicago/Turabian Style
Ji, X., Shan Zeng and Yuzhu Wang. 2024 "Mineral Identification based on Out-Of-Distribution Detection" Preprints. https://doi.org/10.20944/preprints202405.0312.v1
Abstract
Deep learning has increasingly been employed to identify minerals. However, deep learning can only be used to identify minerals in the distribution of the training set, while any mineral outside the spectrum of the training set is inevitably categorized erroneously within a predetermined class from the training set. To solve this problem, the study introduces the approach that amalgamates One-Class Support Vector Machines (OCSVM) with the ResNet architecture for the out-of-distribution mineral detection. Initially, ResNet undergoes training using a training set comprising well-defined minerals. Subsequently, the earlier layers of the trained ResNet are employed to extract the discriminative features of the mineral under consideration. These extracted mineral features then become the input for OCSVM. When OCSVM discerns the mineral in the training set's distribution, it triggers the subsequent layers within the trained ResNet, facilitating the accurate classification of the mineral into one of the predefined categories encompassing the known minerals. In the event OCSVM identifies the mineral out of the training set's distribution, it is unequivocally categorized as an unclassified or 'unknown' mineral. Empirical results substantiate the method's capability to identify out-of-distribution minerals while concurrently maintaining a commendably high accuracy rate for the classification of the 36 in-distribution minerals.
Keywords
mineral identification; deep learning; out-of-distribution detection; one-class support vector machines (OCSVM); ResNet
Subject
Environmental and Earth Sciences, Geophysics and Geology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.