20 citations · 32 across the 18 of their papers we have counts for
5 papers · 1 filter
Learned representation-guided diffusion models for large-image generation
Alexandros Graikos, Srikar Yellapragada, Minh-Quan Le +4
To synthesize high-fidelity samples, diffusion models typically require auxiliary data to guide the generation process. However, it is impractical to procure the painstaking patch-…
PathLDM: Text conditioned Latent Diffusion Model for Histopathology
Srikar Yellapragada, Alexandros Graikos, Prateek Prasanna +3
To achieve high-quality results, diffusion models must be trained on large datasets. This can be notably prohibitive for models in specialized domains, such as computational pathol…
Conditional Generation from Unconditional Diffusion Models using Denoiser Representations
Alexandros Graikos, Srikar Yellapragada, Dimitris Samaras
Denoising diffusion models have gained popularity as a generative modeling technique for producing high-quality and diverse images. Applying these models to downstream tasks requir…
S-VolSDF: Sparse Multi-View Stereo Regularization of Neural Implicit Surfaces
Haoyu Wu, Alexandros Graikos, Dimitris Samaras
Neural rendering of implicit surfaces performs well in 3D vision applications. However, it requires dense input views as supervision. When only sparse input images are available, o…
GFlowNet-EM for learning compositional latent variable models
Edward J. Hu, Nikolay Malkin, Moksh Jain +3
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 l…