Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Laplacian Pyramid of Adversarial Networks (LAP-GAN) <s> We describe a technique for image encoding in which local operators of many scales but identical shape serve as the basis functions. The representation differs from established techniques in that the code elements are localized in spatial frequency as well as in space. Pixel-to-pixel correlations are first removed by subtracting a lowpass filtered copy of the image from the image itself. The result is a net data compression since the difference, or error, image has low variance and entropy, and the low-pass filtered image may represented at reduced sample density. Further data compression is achieved by quantizing the difference image. These steps are then repeated to compress the low-pass image. Iteration of the process at appropriately expanded scales generates a pyramid data structure. The encoding process is equivalent to sampling the image with Laplacian operators of many scales. Thus, the code tends to enhance salient image features. A further advantage of the present code is that it is well suited for many image analysis tasks as well as for image compression. Fast algorithms are described for coding and decoding. <s> BIB001 </s> Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Laplacian Pyramid of Adversarial Networks (LAP-GAN) <s> In this paper we introduce a generative parametric model capable of producing high quality samples of natural images. Our approach uses a cascade of convolutional networks within a Laplacian pyramid framework to generate images in a coarse-to-fine fashion. At each level of the pyramid, a separate generative convnet model is trained using the Generative Adversarial Nets (GAN) approach [11]. Samples drawn from our model are of significantly higher quality than alternate approaches. In a quantitative assessment by human evaluators, our CIFAR10 samples were mistaken for real images around 40% of the time, compared to 10% for samples drawn from a GAN baseline model. We also show samples from models trained on the higher resolution images of the LSUN scene dataset. <s> BIB002
LAPGAN is proposed for the production of higher resolution images from lower resolution input GAN BIB002 . Figure. 3 demonstrates the up-sampling process of generator in LAP-GAN from right to left. LAPGAN utilizes a cascade of CNNs within a Laplacian pyramid framework BIB001 to generate high quality images.