Ways of Conditioning Generative Adversarial Networks
This work addresses a gap in GAN research for conditional generation, offering improved methods for applications requiring attribute-specific image synthesis.
The paper tackles the problem of conditioning generative adversarial networks (GANs) to generate samples with specific attributes, achieving state-of-the-art results on MNIST and CIFAR-10 datasets.
The GANs are generative models whose random samples realistically reflect natural images. It also can generate samples with specific attributes by concatenating a condition vector into the input, yet research on this field is not well studied. We propose novel methods of conditioning generative adversarial networks (GANs) that achieve state-of-the-art results on MNIST and CIFAR-10. We mainly introduce two models: an information retrieving model that extracts conditional information from the samples, and a spatial bilinear pooling model that forms bilinear features derived from the spatial cross product of an image and a condition vector. These methods significantly enhance log-likelihood of test data under the conditional distributions compared to the methods of concatenation.