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Found 3 papers, 2 papers with code
Exploiting GAN Internal Capacity for High-Quality Reconstruction of Natural Images
Finally, as an example of potential applications that arise from this inversion mechanism, we show preliminary results in exploiting the learned representation in the attention map of the generator to obtain an unsupervised segmentation of natural images.
Exploiting video sequences for unsupervised disentangling in generative adversarial networks
In this work we present an adversarial training algorithm that exploits correlations in video to learn --without supervision-- an image generator model with a disentangled latent space.
Class-Splitting Generative Adversarial Networks
Generative Adversarial Networks (GANs) produce systematically better quality samples when class label information is provided., i. e. in the conditional GAN setup.
Ranked #8 on
Conditional Image Generation
on CIFAR-10
(Inception score metric)
