  Deep Learning performs well when training data densely covers the experience
space. For complex problems this makes data collection prohibitively expensive.
We propose to intelligently select samples when constructing data sets in order
to best utilize the available labeling budget. The selection methodology is
based on the presumption that two dissimilar samples are worth more than two
similar samples in a data set. Similarity is measured based on the Euclidean
distance between samples in the latent space produced by a DNN. By using a
self-supervised method to construct the latent space, it is ensured that the
space fits the data well and that any upfront labeling effort can be avoided.
The result is more efficient, diverse, and balanced data set, which produce
equal or superior results with fewer labeled examples.
