Research Project Title:
Investigating One-Shot and Few-Shot Learning for Segmentation Tasks
abstract:Currently, many biologists must annotate large quantities of data before deep learning can be applied to tasks, and the extra burden in finding objects decreases the likelihood an image can be used for biological discovery. Fortunately, techniques such as few-shot learning can greatly reduce the amount of annotated data needed to train a network. Although these techniques are well-researched for deep learning classification tasks, their application to segmentation or detection has been less explored. My research aims to generalize few-shot learning strategies to new classes of architectures, transforming them from classification tools to segmentation or detection tools. Once implemented, the degree of performance improvement achieved can be evaluated using real biological image data.
My hope is that, through SuperUROP, I will gain authentic research experience that I can use to make more informed future career decisions. I'm very grateful to be working on the cutting edge of machine learning as applied to biology and mentored by leaders of the field, and I'm excited to look back at the end of this academic year and see that I've accomplished something meaningful.