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Found 5 papers, 5 papers with code
High Fidelity Image Synthesis With Deep VAEs In Latent Space
With this method, the VAE avoids modeling the fine-grained details that constitute the majority of the image's code length, allowing it to focus on learning its structural components.
Optimizing Hierarchical Image VAEs for Sample Quality
To address this, we introduce a KL-reweighting strategy to control the amount of infor mation in each latent group, and employ a Gaussian output layer to reduce sharpness in the learning objective.
Improving Diffusion Model Efficiency Through Patching
Diffusion models are a powerful class of generative models that iteratively denoise samples to produce data.
Knowledge Distillation in Iterative Generative Models for Improved Sampling Speed
Iterative generative models, such as noise conditional score networks and denoising diffusion probabilistic models, produce high quality samples by gradually denoising an initial noise vector.
Ranked #85 on
Image Generation
on CIFAR-10
Diffusion models for Handwriting Generation
In this paper, we propose a diffusion probabilistic model for handwriting generation.
