How much data do you need? Part 2: Predicting DL class specific training dataset sizes
This work addresses a combinatorial challenge in dataset design for machine learning practitioners, but it appears incremental as it builds on existing modeling approaches without demonstrating broad impact.
The paper tackles the problem of predicting classification model performance based on per-class training example counts rather than total dataset size, proposing an algorithm inspired by space-filling design of experiments and modeling results with extended power-law curves. It was applied to CIFAR10 and EMNIST datasets, but no concrete performance numbers are provided.
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.