A Triangle Generative Adversarial Network ($\Delta $-GAN) is developed for semi-supervised
cross-domain joint distribution matching, where the training data consists of samples from …

We propose a unified game-theoretical framework to perform classification and conditional
image generation given limited supervision. It is formulated as a three-player minimax game …

… same network architecture as used in Triple GAN [2]. For the edges2shoes dataset, we use
the same network … For other datasets, we provide the detailed network architectures below. …

… triangle as a framework for joint training of a generator model, energy-based model and
inference model. The divergence triangle … demonstrate that the divergence triangle is capable of …

… triangles in a signed network, our framework learns not only the edges in balanced real-triangles
but those in balanced virtual-triangles that … Finally, our framework employs adversarial …

… Similar oracle inequality and density estimation performance for Generative Adversarial
Networks are also studied in [19]. Here we … Indeed, it follows from the triangle inequality that …

… triangle as a framework for joint training of generator model, energy-based model and inference
model. The divergence triangle is … deep convolutional generative adversarial networks,” …

… generative adversarial network (GAN), called SphereGAN. In the … architecture for generative
adversarial networks,” in Proc. … Kwon, “3D point cloud generative adversarial network based …

… is not taken into account by existing generative models. This paper introduces the
Geometry-Aware Generative Adversarial Networks (GAGAN) for incorporating geometric information …

The authors present a generative adversarial network (GAN) model that demonstrates how
to generate 3D models in their native format so that they can be either evaluated using …