Search Results for author:
Found 8 papers, 3 papers with code
SINE: SINgle Image Editing with Text-to-Image Diffusion Models
We propose a novel model-based guidance built upon the classifier-free guidance so that the knowledge from the model trained on a single image can be distilled into the pre-trained diffusion model, enabling content creation even with one given image.
STEER : Simple Temporal Regularization For Neural ODE
Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive.
STEER: Simple Temporal Regularization For Neural ODEs
Training Neural Ordinary Differential Equations (ODEs) is often computationally expensive.
Interactive Sketch & Fill: Multiclass Sketch-to-Image Translation
We propose an interactive GAN-based sketch-to-image translation method that helps novice users create images of simple objects.
DGPose: Deep Generative Models for Human Body Analysis
However, the latent space learned by such approaches is typically not interpretable, resulting in less flexibility.
Multi-Agent Diverse Generative Adversarial Networks
Second, to enforce that different generators capture diverse high probability modes, the discriminator of MAD-GAN is designed such that along with finding the real and fake samples, it is also required to identify the generator that generated the given fake sample.
Message Passing Multi-Agent GANs
As a first step towards this challenge, we introduce a novel framework for image generation: Message Passing Multi-Agent Generative Adversarial Networks (MPM GANs).
Contextual RNN-GANs for Abstract Reasoning Diagram Generation
Understanding, predicting, and generating object motions and transformations is a core problem in artificial intelligence.
