83 citations · 242 across the 10 of their papers we have counts for
10 papers
Building robust classifiers through generation of confident out of distribution examples
Kumar Sricharan, Ashok Srivastava
Deep learning models are known to be overconfident in their predictions on out of distribution inputs. There have been several pieces of work to address this issue, including a num…
Improving robustness of classifiers by training against live traffic
Kumar Sricharan, Kumar Kallurupalli, Ashok Srivastava
Deep learning models are known to be overconfident in their predictions on out of distribution inputs. This is a challenge when a model is trained on a particular input dataset, bu…
ExprGAN: Facial Expression Editing with Controllable Expression Intensity
Hui Ding, Kumar Sricharan, Rama Chellappa
Facial expression editing is a challenging task as it needs a high-level semantic understanding of the input face image. In conventional methods, either paired training data is req…
Semi-supervised Conditional GANs
Kumar Sricharan, Raja Bala, Matthew Shreve +3
We introduce a new model for building conditional generative models in a semi-supervised setting to conditionally generate data given attributes by adapting the GAN framework. The…
Ensemble Estimation of Distributional Functionals via -Nearest Neighbors
Kevin R. Moon, Kumar Sricharan, Alfred O. Hero
The problem of accurate nonparametric estimation of distributional functionals (integral functionals of one or more probability distributions) has received recent interest due to t…
Recognizing Abnormal Heart Sounds Using Deep Learning
Jonathan Rubin, Rui Abreu, Anurag Ganguli +3
The work presented here applies deep learning to the task of automated cardiac auscultation, i.e. recognizing abnormalities in heart sounds. We describe an automated heart sound cl…