Search Results for author:
Found 12 papers, 6 papers with code
Diffusion Guided Domain Adaptation of Image Generators
Can a text-to-image diffusion model be used as a training objective for adapting a GAN generator to another domain?
Shifted Diffusion for Text-to-image Generation
Unlike the baseline diffusion model used in DALL-E 2, our method seamlessly encodes prior knowledge of the pre-trained CLIP model in its diffusion process by designing a new initialization distribution and a new transition step of the diffusion.
Ranked #3 on
Text-to-Image Generation
on Multi-Modal-CelebA-HQ
PIVQGAN: Posture and Identity Disentangled Image-to-Image Translation via Vector Quantization
The VQSN module facilitates a more delicate separation of posture and identity, while the training scheme ensures the VQSN module learns the pose-related representations.
Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis
Training Generative Adversarial Networks (GAN) on high-fidelity images usually requires large-scale GPU-clusters and a vast number of training images.
Ranked #2 on
Image Generation
on ADE-Indoor
Self-Supervised Sketch-to-Image Synthesis
Moreover, with the proposed sketch generator, the model shows a promising performance on style mixing and style transfer, which require synthesized images to be both style-consistent and semantically meaningful.
TIME: Text and Image Mutual-Translation Adversarial Networks
Focusing on text-to-image (T2I) generation, we propose Text and Image Mutual-Translation Adversarial Networks (TIME), a lightweight but effective model that jointly learns a T2I generator G and an image captioning discriminator D under the Generative Adversarial Network framework.
Sketch-to-Art: Synthesizing Stylized Art Images From Sketches
We propose a new approach for synthesizing fully detailed art-stylized images from sketches.
OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization
Exploring the potential of GANs for unsupervised disentanglement learning, this paper proposes a novel GAN-based disentanglement framework with One-Hot Sampling and Orthogonal Regularization (OOGAN).
Learning Feature-to-Feature Translator by Alternating Back-Propagation for Generative Zero-Shot Learning
We investigate learning feature-to-feature translator networks by alternating back-propagation as a general-purpose solution to zero-shot learning (ZSL) problems.
A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts
Most existing zero-shot learning methods consider the problem as a visual semantic embedding one.
CAN: Creative Adversarial Networks, Generating "Art" by Learning About Styles and Deviating from Style Norms
We argue that such networks are limited in their ability to generate creative products in their original design.
