  One of the biggest bottlenecks in a machine learning workflow is waiting for
models to train. Depending on the available computing resources, it can take
days to weeks to train a neural network on a large dataset with many classes
such as ImageNet. For researchers experimenting with new algorithmic
approaches, this is impractically time consuming and costly. We aim to generate
smaller "proxy datasets" where experiments are cheaper to run but results are
highly correlated with experimental results on the full dataset. We generate
these proxy datasets using by randomly sampling from examples or classes,
training on only the easiest or hardest examples and training on synthetic
examples generated by "data distillation". We compare these techniques to the
more widely used baseline of training on the full dataset for fewer epochs. For
each proxying strategy, we estimate three measures of "proxy quality": how much
of the variance in experimental results on the full dataset can be explained by
experimental results on the proxy dataset.
  Experiments on Imagenette and Imagewoof (Howard, 2019) show that running
hyperparameter search on the easiest 10% of examples explains 81% of the
variance in experiment results on the target task, and using the easiest 50% of
examples can explain 95% of the variance, significantly more than training on
all the data for fewer epochs, a more widely used baseline. These "easy"
proxies are higher quality than training on the full dataset for a reduced
number of epochs (but equivalent computational cost), and, unexpectedly, higher
quality than proxies constructed from the hardest examples. Without access to a
trained model, researchers can improve proxy quality by restricting the subset
to fewer classes; proxies built on half the classes are higher quality than
those with an equivalent number of examples spread across all classes.
