We demonstrate a method for training a convolutional neural network with simulated images for usage on real-world experimental data. Modern machine learning methods require large, robust training data sets to generate accurate predictions. Generating these large training sets requires a significant up-front time investment that is often impractical for small-scale applications. Here we demonstrate a `full-stack' computational solution, where the training data set is generated on-the-fly using a noise injection process to produce simulated data characteristic of the experimental system.
@article{arxiv.1908.05271,
title = {End-to-End Machine Learning for Experimental Physics: Using Simulated Data to Train a Neural Network for Object Detection in Video Microscopy},
author = {Eric N. Minor and Stian D. Howard and Adam A. S. Green and Cheol S. Park and Noel A. Clark},
journal= {arXiv preprint arXiv:1908.05271},
year = {2019}
}