  Recently, subsampling or refining images generated from unconditional GANs
has been actively studied to improve the overall image quality. Unfortunately,
these methods are often observed less effective or inefficient in handling
conditional GANs (cGANs) -- conditioning on a class (aka class-conditional
GANs) or a continuous variable (aka continuous cGANs or CcGANs). In this work,
we introduce an effective and efficient subsampling scheme, named conditional
density ratio-guided rejection sampling (cDR-RS), to sample high-quality images
from cGANs. Specifically, we first develop a novel conditional density ratio
estimation method, termed cDRE-F-cSP, by proposing the conditional Softplus
(cSP) loss and an improved feature extraction mechanism. We then derive the
error bound of a density ratio model trained with the cSP loss. Finally, we
accept or reject a fake image in terms of its estimated conditional density
ratio. A filtering scheme is also developed to increase fake images' label
consistency without losing diversity when sampling from CcGANs. We extensively
test the effectiveness and efficiency of cDR-RS in sampling from both
class-conditional GANs and CcGANs on five benchmark datasets. When sampling
from class-conditional GANs, cDR-RS outperforms modern state-of-the-art methods
by a large margin (except DRE-F-SP+RS) in terms of effectiveness. Although the
effectiveness of cDR-RS is often comparable to that of DRE-F-SP+RS, cDR-RS is
substantially more efficient. When sampling from CcGANs, the superiority of
cDR-RS is even more noticeable in terms of both effectiveness and efficiency.
Notably, with the consumption of reasonable computational resources, cDR-RS can
substantially reduce Label Score without decreasing the diversity of
CcGAN-generated images, while other methods often need to trade much diversity
for slightly improved Label Score.
