  Subsampling unconditional generative adversarial networks (GANs) to improve
the overall image quality has been studied recently. However, these methods
often require high training costs (e.g., storage space, parameter tuning) and
may be inefficient or even inapplicable for subsampling conditional GANs, such
as class-conditional GANs and continuous conditional GANs (CcGANs), when the
condition has many distinct values. In this paper, we propose an efficient
method called conditional density ratio estimation in feature space with
conditional Softplus loss (cDRE-F-cSP). With cDRE-F-cSP, we estimate an image's
conditional density ratio based on a novel conditional Softplus (cSP) loss in
the feature space learned by a specially designed ResNet-34 or sparse
autoencoder. We then derive the error bound of a conditional density ratio
model trained with the proposed cSP loss. Finally, we propose a rejection
sampling scheme, termed cDRE-F-cSP+RS, which can subsample both
class-conditional GANs and CcGANs efficiently. An extra filtering scheme is
also developed for CcGANs to increase the label consistency. Experiments on
CIFAR-10 and Tiny-ImageNet datasets show that cDRE-F-cSP+RS can substantially
improve the Intra-FID and FID scores of BigGAN. Experiments on RC-49 and
UTKFace datasets demonstrate that cDRE-F-cSP+RS also improves Intra-FID,
Diversity, and Label Score of CcGANs. Moreover, to show the high efficiency of
cDRE-F-cSP+RS, we compare it with the state-of-the-art unconditional
subsampling method (i.e., DRE-F-SP+RS). With comparable or even better
performance, cDRE-F-cSP+RS only requires about \textbf{10}\% and \textbf{1.7}\%
of the training costs spent respectively on CIFAR-10 and UTKFace by
DRE-F-SP+RS.
