  This paper introduces a novel convolution method, called generative
convolution (GConv), which is simple yet effective for improving the generative
adversarial network (GAN) performance. Unlike the standard convolution, GConv
first selects useful kernels compatible with the given latent vector, and then
linearly combines the selected kernels to make latent-specific kernels. Using
the latent-specific kernels, the proposed method produces the latent-specific
features which encourage the generator to produce high-quality images. This
approach is simple but surprisingly effective. First, the GAN performance is
significantly improved with a little additional hardware cost. Second, GConv
can be employed to the existing state-of-the-art generators without modifying
the network architecture. To reveal the superiority of GConv, this paper
provides extensive experiments using various standard datasets including
CIFAR-10, CIFAR-100, LSUN-Church, CelebA, and tiny-ImageNet. Quantitative
evaluations prove that GConv significantly boosts the performances of the
unconditional and conditional GANs in terms of Inception score (IS) and Frechet
inception distance (FID). For example, the proposed method improves both FID
and IS scores on the tiny-ImageNet dataset from 35.13 to 29.76 and 20.23 to
22.64, respectively.
