  Generative Adversarial Networks (GANs) is a powerful family of models that
learn an underlying distribution to generate synthetic data. Many existing
studies of GANs focus on improving the realness of the generated image data for
visual applications, and few of them concern about improving the quality of the
generated data for training other classifiers -- a task known as the model
compatibility problem. As a consequence, existing GANs often prefer generating
`easier' synthetic data that are far from the boundaries of the classifiers,
and refrain from generating near-boundary data, which are known to play an
important roles in training the classifiers. To improve GAN in terms of model
compatibility, we propose Boundary-Calibration GANs (BCGANs), which leverage
the boundary information from a set of pre-trained classifiers using the
original data. In particular, we introduce an auxiliary Boundary-Calibration
loss (BC-loss) into the generator of GAN to match the statistics between the
posterior distributions of original data and generated data with respect to the
boundaries of the pre-trained classifiers. The BC-loss is provably unbiased and
can be easily coupled with different GAN variants to improve their model
compatibility. Experimental results demonstrate that BCGANs not only generate
realistic images like original GANs but also achieves superior model
compatibility than the original GANs.
