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Found 8 papers, 5 papers with code
Learned representation-guided diffusion models for large-image generation
Generating images from learned embeddings is agnostic to the source of the embeddings.
PathLDM: Text conditioned Latent Diffusion Model for Histopathology
To achieve high-quality results, diffusion models must be trained on large datasets.
Conditional Generation from Unconditional Diffusion Models using Denoiser Representations
Our approach provides a powerful and flexible way to adapt diffusion models to new conditions and generate high-quality augmented data for various conditional generation tasks.
S-VolSDF: Sparse Multi-View Stereo Regularization of Neural Implicit Surfaces
We thus propose to regularize neural rendering optimization with an MVS solution.
GFlowNet-EM for learning compositional latent variable models
Latent variable models (LVMs) with discrete compositional latents are an important but challenging setting due to a combinatorially large number of possible configurations of the latents.
Diffusion models as plug-and-play priors
We consider the problem of inferring high-dimensional data $\mathbf{x}$ in a model that consists of a prior $p(\mathbf{x})$ and an auxiliary differentiable constraint $c(\mathbf{x},\mathbf{y})$ on $x$ given some additional information $\mathbf{y}$.
Resolving label uncertainty with implicit posterior models
We propose a method for jointly inferring labels across a collection of data samples, where each sample consists of an observation and a prior belief about the label.
Resolving label uncertainty with implicit generative models
In prediction problems, coarse and imprecise sources of input can provide rich information about labels, but are not readily used by discriminative learners.
