1 citations · 2 across the 6 of their papers we have counts for
6 papers
A Likely Geometry of Generative Models
Frederik Möbius Rygaard, Shen Zhu, Yinzhu Jin +2
The geometry of generative models serves as the basis for interpolation, model inspection, and more. Unfortunately, most generative models lack a principal notion of geometry witho…
Point-Based Shape Representation Generation with a Correspondence-Preserving Diffusion Model
Shen Zhu, Yinzhu Jin, Ifrah Zawar +1
We propose a diffusion model designed to generate point-based shape representations with correspondences. Traditional statistical shape models have considered point correspondences…
RealDeal: Enhancing Realism and Details in Brain Image Generation via Image-to-Image Diffusion Models
Shen Zhu, Yinzhu Jin, Tyler Spears +2
We propose image-to-image diffusion models that are designed to enhance the realism and details of generated brain images by introducing sharp edges, fine textures, subtle anatomic…
MedIL: Implicit Latent Spaces for Generating Heterogeneous Medical Images at Arbitrary Resolutions
Tyler Spears, Shen Zhu, Yinzhu Jin +2
In this work, we introduce MedIL, a first-of-its-kind autoencoder built for encoding medical images with heterogeneous sizes and resolutions for image generation. Medical images ar…
Measuring Feature Dependency of Neural Networks by Collapsing Feature Dimensions in the Data Manifold
Yinzhu Jin, Matthew B. Dwyer, P. Thomas Fletcher
This paper introduces a new technique to measure the feature dependency of neural network models. The motivation is to better understand a model by querying whether it is using inf…
Feature Gradient Flow for Interpreting Deep Neural Networks in Head and Neck Cancer Prediction
Yinzhu Jin, Jonathan C. Garneau, P. Thomas Fletcher
This paper introduces feature gradient flow, a new technique for interpreting deep learning models in terms of features that are understandable to humans. The gradient flow of a mo…