Human-efficient Discovery of Training Data for Visual Machine Learning

Deep learning has enabled valuable computer vision applications in many domains. It can be used to assist domain experts, such as scientists, military, and medical doctors, to detect phenomena of interest quickly. Unfortunately, the manual effort to collect large training sets of domain-specific targets remains a deterrent to deep learning in these domains. Crowd-sourcing is not a viable solution, because the crowds do not have the professional knowledge to accurately label examples. Interesting objects are usually scarce. As a result, a single expert may need to go through millions of unlabeled examples to find a few positive instances of a target.