Upcoming EMC Talk on Few-Shot Image Analysis

Join me next week at the Electronic Materials Conference 2021! On Thursday, June 24th @ 1700 EDT I’ll be presenting a talk on “Rapid and Flexible Few Shot Learning-Based Classification of Scanning Transmission Electron Microscopy Data” in Symposium V07.

From the abstract:

Control of property-defining materials defects for quantum computing and energy storage depends on the ability to precisely probe structure and chemistry at the highest spatial and temporal resolutions. Modern scanning transmission electron microscopy (STEM) is well-suited to this task, having yielded rich insights into defect populations in many systems. However, the dilute nature and complexity of materials defects, coupled with their varied representations in STEM data, makes reliable, accurate, high-throughput statistical defect analysis a significant challenge. Possible analysis approaches include low-level pixel processing, or even the application of machine learning methods for classification and image segmentation. However, the latter requires large sets of labeled training data that are difficult to obtain for many practical materials science studies. Here, we describe the use of an emerging few shot learning capability for rapid and flexible STEM data classification. This approach requires minimal information at the start of the analysis and uses a generally pre-trained encoder network to make inferences on experimental data. Our results show drastic improvements in data annotation costs, reproducibility, and scalability in comparison to neural network training from scratch. We demonstrate how few shot techniques can quickly extract feature maps and global statistics from a variety of STEM data, enabling a new quantitative understanding of defect populations.

To learn more about the conference, visit the EMC Program Website.