Search Results for author:
Found 11 papers, 10 papers with code
Collaborative Learning of Bidirectional Decoders for Unsupervised Text Style Transfer
In this paper, we propose a collaborative learning framework for unsupervised text style transfer using a pair of bidirectional decoders, one decoding from left to right while the other decoding from right to left.
Cross Initialization for Personalized Text-to-Image Generation
Additionally, a fast version of our method allows for capturing an input image in roughly 26 seconds, while surpassing the baseline methods in terms of both reconstruction and editability.
Cycle Encoding of a StyleGAN Encoder for Improved Reconstruction and Editability
The recently proposed pivotal tuning model makes significant progress towards reconstruction and editability, by using a two-step approach that first inverts the input image into a latent code, called pivot code, and then alters the generator so that the input image can be accurately mapped into the pivot code.
Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme
In this work, we revisit the role of discriminator in GAN compression and design a novel generator-discriminator cooperative compression scheme for GAN compression, termed GCC.
Generative Semi-supervised Learning for Multivariate Time Series Imputation
In this paper, we propose a novel semi-supervised generative adversarial network model, named SSGAN, for missing value imputation in multivariate time series data.
Generative Adversarial Network
Multivariate Time Series Imputation
+2
Image-to-image Translation via Hierarchical Style Disentanglement
Recently, image-to-image translation has made significant progress in achieving both multi-label (\ie, translation conditioned on different labels) and multi-style (\ie, generation with diverse styles) tasks.
Disentanglement
Multimodal Unsupervised Image-To-Image Translation
+1
Virtual Mixup Training for Unsupervised Domain Adaptation
Existing methods only impose the locally-Lipschitz constraint around the training points while miss the other areas, such as the points in-between training data.
Unpaired Multi-Domain Image Generation via Regularized Conditional GANs
To tackle this problem, we propose Regularized Conditional GAN (RegCGAN) which is capable of learning to generate corresponding images in the absence of paired training data.
On the Effectiveness of Least Squares Generative Adversarial Networks
To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss for both the discriminator and the generator.
AlignGAN: Learning to Align Cross-Domain Images with Conditional Generative Adversarial Networks
Recently, several methods based on generative adversarial network (GAN) have been proposed for the task of aligning cross-domain images or learning a joint distribution of cross-domain images.
Least Squares Generative Adversarial Networks
To overcome such a problem, we propose in this paper the Least Squares Generative Adversarial Networks (LSGANs) which adopt the least squares loss function for the discriminator.
