  This paper presents our work on "SNaCK," a low-dimensional concept embedding
algorithm that combines human expertise with automatic machine similarity
kernels. Both parts are complimentary: human insight can capture relationships
that are not apparent from the object's visual similarity and the machine can
help relieve the human from having to exhaustively specify many constraints. We
show that our SNaCK embeddings are useful in several tasks: distinguishing
prime and nonprime numbers on MNIST, discovering labeling mistakes in the
Caltech UCSD Birds (CUB) dataset with the help of deep-learned features,
creating training datasets for bird classifiers, capturing subjective human
taste on a new dataset of 10,000 foods, and qualitatively exploring an
unstructured set of pictographic characters. Comparisons with the
state-of-the-art in these tasks show that SNaCK produces better concept
embeddings that require less human supervision than the leading methods.
