  Generative Adversarial Networks (GANs) have been used widely to generate
large volumes of synthetic data. This data is being utilized for augmenting
with real examples in order to train deep Convolutional Neural Networks (CNNs).
Studies have shown that the generated examples lack sufficient realism to train
deep CNNs and are poor in diversity. Unlike previous studies of randomly
augmenting the synthetic data with real data, we present our simple, effective
and easy to implement synthetic data sampling methods to train deep CNNs more
efficiently and accurately. To this end, we propose to maximally utilize the
parameters learned during training of the GAN itself. These include
discriminator's realism confidence score and the confidence on the target label
of the synthetic data. In addition to this, we explore reinforcement learning
(RL) to automatically search a subset of meaningful synthetic examples from a
large pool of GAN synthetic data. We evaluate our method on two challenging
face attribute classification data sets viz. AffectNet and CelebA. Our
extensive experiments clearly demonstrate the need of sampling synthetic data
before augmentation, which also improves the performance of one of the
state-of-the-art deep CNNs in vitro.
