Experimental Design for Bathymetry Editing

Abstract

We describe an application of machine learning to a real-world computer assisted labeling task. Our experimental results expose significant deviations from the IID assumption commonly used in machine learning. These results suggest that the common random split of all data into training and testing can often lead to poor performance.

Published as a workshop paper at ICML 2020 Workshop on Real World Experiment Design and Active Learning