  Understanding the mechanism of generative adversarial networks (GANs) helps
us better use GANs for downstream applications. Existing efforts mainly target
interpreting unconditional models, leaving it less explored how a conditional
GAN learns to render images regarding various categories. This work fills in
this gap by investigating how a class conditional generator unifies the
synthesis of multiple classes. For this purpose, we dive into the widely used
class-conditional batch normalization (CCBN), and observe that each feature
channel is activated at varying degrees given different categorical embeddings.
To describe such a phenomenon, we propose channel awareness, which
quantitatively characterizes how a single channel contributes to the final
synthesis. Extensive evaluations and analyses on the BigGAN model pre-trained
on ImageNet reveal that only a subset of channels is primarily responsible for
the generation of a particular category, similar categories (e.g., cat and dog)
usually get related to some same channels, and some channels turn out to share
information across all classes. For good measure, our algorithm enables several
novel applications with conditional GANs. Concretely, we achieve (1) versatile
image editing via simply altering a single channel and manage to (2)
harmoniously hybridize two different classes. We further verify that the
proposed channel awareness shows promising potential in (3) segmenting the
synthesized image and (4) evaluating the category-wise synthesis performance.
