Conditional image generation is the task of generating new images from a dataset conditional on their class.
( Image credit: PixelCNN++ )
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Therefore, in order to conduct a thorough study on GANs while obviating unnecessary interferences introduced by the datasets, we train them on artificial datasets where there are infinitely many samples and the real data distributions are simple, high-dimensional and have structured manifolds.
Ranked #1 on Conditional Image Generation on CIFAR-10
Traditional convolution-based generative adversarial networks synthesize images based on hierarchical local operations, where long-range dependency relation is implicitly modeled with a Markov chain.
In this paper, we explore synthesizing person images with multiple conditions for various backgrounds.
We present MixNMatch, a conditional generative model that learns to disentangle and encode background, object pose, shape, and texture from real images with minimal supervision, for mix-and-match image generation.
This can be done by conditioning the model on additional information.
We present two new metrics for evaluating generative models in the class-conditional image generation setting.
A lesion conditional image (segmented mask) is an input to both the generator and the discriminator of the LcGAN during training.
In this study, we proposed a new GAN-based Bayesian visual reconstruction method (GAN-BVRM) that includes a classifier to decode categories from fMRI data, a pre-trained conditional generator to generate natural images of specified categories, and a set of encoding models and evaluator to evaluate generated images.