  Conditional generative adversarial networks (cGANs) have been widely
researched to generate class conditional images using a single generator.
However, in the conventional cGANs techniques, it is still challenging for the
generator to learn condition-specific features, since a standard convolutional
layer with the same weights is used regardless of the condition. In this paper,
we propose a novel convolution layer, called the conditional convolution layer,
which directly generates different feature maps by employing the weights which
are adjusted depending on the conditions. More specifically, in each
conditional convolution layer, the weights are conditioned in a simple but
effective way through filter-wise scaling and channel-wise shifting operations.
In contrast to the conventional methods, the proposed method with a single
generator can effectively handle condition-specific characteristics. The
experimental results on CIFAR, LSUN and ImageNet datasets show that the
generator with the proposed conditional convolution layer achieves a higher
quality of conditional image generation than that with the standard convolution
layer.
