How much data do you need? Part 2: Predicting DL class specific training
  dataset sizes
Thomas Mühlenstädt Jelena Frtunikj
This paper targets the question of predicting machine learning classification
model performance, when taking into account the number of training examples per
class and not just the overall number of training examples. This leads to the a
combinatorial question, which combinations of number of training examples per
class should be considered, given a fixed overall training dataset size. In
order to solve this question, an algorithm is suggested which is motivated from
special cases of space filling design of experiments. The resulting data are
modeled using models like powerlaw curves and similar models, extended like
generalized linear models i.e. by replacing the overall training dataset size
by a parametrized linear combination of the number of training examples per
label class. The proposed algorithm has been applied on the CIFAR10 and the
EMNIST datasets.