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Found 4 papers, 3 papers with code
Stochastic Conditional Diffusion Models for Semantic Image Synthesis
This facilitates the generation of an image close to a clean image, enabling robust generation.
DDMI: Domain-Agnostic Latent Diffusion Models for Synthesizing High-Quality Implicit Neural Representations
Arguably, this architecture limits the expressive power of generative models and results in low-quality INR generation.
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Probabilistic Precision and Recall Towards Reliable Evaluation of Generative Models
Assessing the fidelity and diversity of the generative model is a difficult but important issue for technological advancement.
NaturalInversion: Data-Free Image Synthesis Improving Real-World Consistency
We introduce NaturalInversion, a novel model inversion-based method to synthesize images that agrees well with the original data distribution without using real data.
