Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Boundary Equilibrium GAN (BEGAN) <s> Generative Adversarial Nets [8] were recently introduced as a novel way to train generative models. In this work we introduce the conditional version of generative adversarial nets, which can be constructed by simply feeding the data, y, we wish to condition on to both the generator and discriminator. We show that this model can generate MNIST digits conditioned on class labels. We also illustrate how this model could be used to learn a multi-modal model, and provide preliminary examples of an application to image tagging in which we demonstrate how this approach can generate descriptive tags which are not part of training labels. <s> BIB001 </s> Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Boundary Equilibrium GAN (BEGAN) <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 </s> Generative Adversarial Networks in Computer Vision: A Survey and Taxonomy <s> Boundary Equilibrium GAN (BEGAN) <s> We introduce the "Energy-based Generative Adversarial Network" model (EBGAN) which views the discriminator as an energy function that attributes low energies to the regions near the data manifold and higher energies to other regions. Similar to the probabilistic GANs, a generator is seen as being trained to produce contrastive samples with minimal energies, while the discriminator is trained to assign high energies to these generated samples. Viewing the discriminator as an energy function allows to use a wide variety of architectures and loss functionals in addition to the usual binary classifier with logistic output. Among them, we show one instantiation of EBGAN framework as using an auto-encoder architecture, with the energy being the reconstruction error, in place of the discriminator. We show that this form of EBGAN exhibits more stable behavior than regular GANs during training. We also show that a single-scale architecture can be trained to generate high-resolution images. <s> BIB003
BEGAN uses an autoencoder architecture for the discriminator which was first proposed in EBGAN BIB003 (see Fig. 5 ). Compared to traditional optimization, the BEGAN matches the autoencoder loss distributions using a loss derived from the Wasserstein distance instead of matching data distributions directly. This modification helps G to generate easyto-reconstruct data for the autoencoder at the beginning because the generated data is close to 0 and the real data distribution has not been learned accurately yet, which prevents D easily winning G at the early training stage. . The up-sampling process is marked using green arrow and a conditioning process via a conditional GAN (CGAN) BIB001 is marked using the orange arrow. The process initially uses G 3 to generate image I 3 and then upsamples the image I 3 to l 2 . Together with another noise z 2 , G 2 generates a difference image h 2 and adds h 2 to l 2 which produces the generated image I 2 . The rest can be done in the same manner. LAPGAN contains 3 generators in this work in order to up-sample the image. Figure from BIB002 . 