Model editing for distribution shifts in uranium oxide morphological analysis
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Abstract (arXiv)
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 U3O8 aged in humidity chambers and micrographs acquired with different scanning electron microscopes, respectively.
Key facts. No citing papers in the map yet (data through 14 Oct 2026). Builds on 11 of its 34 references that are in the map. First posted on arXiv 22 Jul 2024.
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11 of 34 references.
References · 11
- 2022A ConvNet for the 2020scited by 3,579
- 2022Locating and Editing Factual Associations in GPTcited by 2,691
- 2022Editing Models with Task Arithmeticcited by 1,120
- 2021Fast Model Editing at Scalecited by 498
- 2022Surgical Fine-Tuning Improves Adaptation to Distribution Shiftscited by 205
- 2020Rewriting a Deep Generative Modelcited by 107
- 2021Editing a classifier by rewriting its prediction rulescited by 65
- 2022Discovering Bugs in Vision Models using Off-the-shelf Image Generation and Captioningcited by 45
- 2023Identifying and Mitigating Model Failures through Few-shot CLIP-aided Diffusion Generationcited by 3
- 2022DBCal: Density Based Calibration of classifier predictions for uncertainty quantificationcited by 1
- 2022Improving Data-Efficient Fossil Segmentation via Model Editingcited by 1
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