  In this paper, we introduce novel lightweight generative adversarial
networks, which can effectively capture long-range dependencies in the image
generation process, and produce high-quality results with a much simpler
architecture. To achieve this, we first introduce a long-range module, allowing
the network to dynamically adjust the number of focused sampling pixels and to
also augment sampling locations. Thus, it can break the limitation of the fixed
geometric structure of the convolution operator, and capture long-range
dependencies in both spatial and channel-wise directions. Also, the proposed
long-range module can highlight negative relations between pixels, working as a
regularization to stabilize training. Furthermore, we propose a new generation
strategy through which we introduce metadata into the image generation process
to provide basic information about target images, which can stabilize and speed
up the training process. Our novel long-range module only introduces few
additional parameters and is easily inserted into existing models to capture
long-range dependencies. Extensive experiments demonstrate the competitive
performance of our method with a lightweight architecture.
