Most existing GANs architectures that generate images use transposed convolution or resize-convolution as their upsampling algorithm from lower to higher resolution feature maps in the generator. We argue that this kind of fixed operation is problematic for GANs to model objects that have very different visual appearances. We propose a novel adaptive convolution method that learns the upsampling algorithm based on the local context at each location to address this problem. We modify a baseline GANs architecture by replacing normal convolutions with adaptive convolutions in the generator. Experiments on CIFAR-10 dataset show that our modified models improve the baseline model by a large margin. Furthermore, our models achieve state-of-the-art performance on CIFAR-10 and STL-10 datasets in the unsupervised setting.
We replace normal convolutions with adaptive convolutions to improve GANs generator. Proposes to replace convolutions in the generator with an Adaptive Convolution Block that learns to generate convolution weigths adn biases of upsampling operations adaptively per pixel location Uses Adaptive Convolution in the context of GANs with a block called AdaConvBlock that replaces regular Convolution, this gives more local context per kernel weight so that it can generate locally flexible objects.