<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Diffusion Models | Vedaant Jain</title><link>https://vedaantjain.netlify.app/tags/diffusion-models/</link><atom:link href="https://vedaantjain.netlify.app/tags/diffusion-models/index.xml" rel="self" type="application/rss+xml"/><description>Diffusion Models</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Sun, 12 May 2024 00:00:00 +0000</lastBuildDate><image><url>https://vedaantjain.netlify.app/media/icon_hu68170e94a17a2a43d6dcb45cf0e8e589_3079_512x512_fill_lanczos_center_3.png</url><title>Diffusion Models</title><link>https://vedaantjain.netlify.app/tags/diffusion-models/</link></image><item><title>Parkinson's Disease Progression</title><link>https://vedaantjain.netlify.app/project/parkinsonsimulation/</link><pubDate>Sun, 12 May 2024 00:00:00 +0000</pubDate><guid>https://vedaantjain.netlify.app/project/parkinsonsimulation/</guid><description>&lt;p>This project addresses limitations in Parkinson&amp;rsquo;s Disease (PD) research by creating synthetic data that aims to simulate changes in facial features associated with PD progression. Using diffusion models and inpainting techniques, we developed a pipeline to generate realistic image pairs representing the transition from healthy to PD-affected facial states. Additionally, we utilized evaluations based on training classification models on the synthetic data. We also explored Model generalization using subset of FFHQ dataset and saw improvement by 5% over previous model baseline.&lt;/p></description></item></channel></rss>