  Current generative networks are increasingly proficient in generating
high-resolution realistic images. These generative networks, especially the
conditional ones, can potentially become a great tool for providing new image
datasets. This naturally brings the question: Can we train a classifier only on
the generated data? This potential availability of nearly unlimited amounts of
training data challenges standard practices for training machine learning
models, which have been crafted across the years for limited and fixed size
datasets. In this work we investigate this question and its related challenges.
We identify ways to improve significantly the performance over naive training
on randomly generated images with regular heuristics. We propose three
standalone techniques that can be applied at different stages of the pipeline,
i.e., data generation, training on generated data, and deploying on real data.
We evaluate our proposed approaches on a subset of the ImageNet dataset and
show encouraging results compared to classifiers trained on real images.
