2.7k citations · 2.9k across the 6 of their papers we have counts for
9 papers · 1 filter
Unconstrained Scene Generation with Locally Conditioned Radiance Fields
Terrance DeVries, Miguel Angel Bautista, Nitish Srivastava +2
We tackle the challenge of learning a distribution over complex, realistic, indoor scenes. In this paper, we introduce Generative Scene Networks (GSN), which learns to decompose sc…
Instance Selection for GANs
Terrance DeVries, Michal Drozdzal, Graham W. Taylor
Recent advances in Generative Adversarial Networks (GANs) have led to their widespread adoption for the purposes of generating high quality synthetic imagery. While capable of gene…
ProxyNCA++: Revisiting and Revitalizing Proxy Neighborhood Component Analysis
Eu Wern Teh, Terrance DeVries, Graham W. Taylor
We consider the problem of distance metric learning (DML), where the task is to learn an effective similarity measure between images. We revisit ProxyNCA and incorporate several en…
On the Evaluation of Conditional GANs
Terrance DeVries, Adriana Romero, Luis Pineda +2
Conditional Generative Adversarial Networks (cGANs) are finding increasingly widespread use in many application domains. Despite outstanding progress, quantitative evaluation of su…
Does Object Recognition Work for Everyone?
Terrance DeVries, Ishan Misra, Changhan Wang +1
The paper analyzes the accuracy of publicly available object-recognition systems on a geographically diverse dataset. This dataset contains household items and was designed to have…
Leveraging Uncertainty Estimates for Predicting Segmentation Quality
Terrance DeVries, Graham W. Taylor
The use of deep learning for medical imaging has seen tremendous growth in the research community. One reason for the slow uptake of these systems in the clinical setting is that t…