Search Results for author:
Found 4 papers, 0 papers with code
Composition and decomposition of GANs
In this work, we propose a composition/decomposition framework for adversarially training generative models on composed data - data where each sample can be thought of as being constructed from a fixed number of components.
3C-GAN: AN CONDITION-CONTEXT-COMPOSITE GENERATIVE ADVERSARIAL NETWORKS FOR GENERATING IMAGES SEPARATELY
We present 3C-GAN: a novel multiple generators structures, that contains one conditional generator that generates a semantic part of an image conditional on its input label, and one context generator generates the rest of an image.
Degrees of Freedom in Deep Neural Networks
The degrees of freedom in deep networks are dramatically smaller than the number of parameters, in some real datasets several orders of magnitude.
PatchMatch Based Joint View Selection and Depthmap Estimation
We propose a multi-view depthmap estimation approach aimed at adaptively ascertaining the pixel level data associations between a reference image and all the elements of a source image set.
