Harm Amplification in Text-to-Image Models

no code implementations • 1 Feb 2024 • Susan Hao, Renee Shelby, Yuchi Liu, Hansa Srinivasan, Mukul Bhutani, Burcu Karagol Ayan, Ryan Poplin, Shivani Poddar, Sarah Laszlo

Text-to-image (T2I) models have emerged as a significant advancement in generative AI; however, there exist safety concerns regarding their potential to produce harmful image outputs even when users input seemingly safe prompts.

SyntheticFur dataset for neural rendering

1 code implementation • 13 May 2021 • Trung Le, Ryan Poplin, Fred Bertsch, Andeep Singh Toor, Margaret L. Oh

We introduce a new dataset called SyntheticFur built specifically for machine learning training.

Generative Adversarial Network Neural Rendering

Likelihood Ratios for Out-of-Distribution Detection

4 code implementations • NeurIPS 2019 • Jie Ren, Peter J. Liu, Emily Fertig, Jasper Snoek, Ryan Poplin, Mark A. DePristo, Joshua V. Dillon, Balaji Lakshminarayanan

We propose a likelihood ratio method for deep generative models which effectively corrects for these confounding background statistics.

Out-of-Distribution Detection Out of Distribution (OOD) Detection

Deep learning for predicting refractive error from retinal fundus images

no code implementations • 21 Dec 2017 • Avinash V. Varadarajan, Ryan Poplin, Katy Blumer, Christof Angermueller, Joe Ledsam, Reena Chopra, Pearse A. Keane, Greg S. Corrado, Lily Peng, Dale R. Webster

Mean absolute error (MAE) of the algorithm's prediction compared to the refractive error obtained in the AREDS and UK Biobank.

Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning

no code implementations • 31 Aug 2017 • Ryan Poplin, Avinash V. Varadarajan, Katy Blumer, Yun Liu, Michael V. McConnell, Greg S. Corrado, Lily Peng, Dale R. Webster

Traditionally, medical discoveries are made by observing associations and then designing experiments to test these hypotheses.

Anatomy