Model Editing for Distribution Shifts in Uranium Oxide Morphological Analysis

Davis Brown, Cody Nizinski, Madelyn Shapiro, Corey Fallon, Tianzhixi Yin, Henry Kvinge, Jonathan H. Tu; Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) Workshops, 2024, pp. 8310-8315

Deep learning still struggles with certain kinds of scientific data. Notably pretraining data may not provide coverage of relevant distribution shifts (e.g. shifts induced via the use of different measurement instruments). We consider deep learning models trained to classify the synthesis conditions of uranium ore concentrates (UOCs) and show that model editing is particularly effective for improving generalization to distribution shifts common in this domain. In particular model editing outperforms finetuning on two curated datasets comprising of micrographs taken of U_ 3 O_ 8 aged in humidity chambers and micrographs acquired with different scanning electron microscopes respectively.