12 citations · 17 across the 3 of their papers we have counts for
6 papers
Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models
Chen Henry Wu, Saman Motamed, Shaunak Srivastava +1
Generative models (e.g., GANs, diffusion models) learn the underlying data distribution in an unsupervised manner. However, many applications of interest require sampling from a pa…
Vanishing Twin GAN: How training a weak Generative Adversarial Network can improve semi-supervised image classification
Saman Motamed, Farzad Khalvati
Generative Adversarial Networks can learn the mapping of random noise to realistic images in a semi-supervised framework. This mapping ability can be used for semi-supervised image…
Multi-class Generative Adversarial Nets for Semi-supervised Image Classification
Saman Motamed, Farzad Khalvati
From generating never-before-seen images to domain adaptation, applications of Generative Adversarial Networks (GANs) spread wide in the domain of vision and graphics problems. Wit…
RANDGAN: Randomized Generative Adversarial Network for Detection of COVID-19 in Chest X-ray
Saman Motamed, Patrik Rogalla, Farzad Khalvati
COVID-19 spread across the globe at an immense rate has left healthcare systems incapacitated to diagnose and test patients at the needed rate. Studies have shown promising results…
Data Augmentation using Generative Adversarial Networks (GANs) for GAN-based Detection of Pneumonia and COVID-19 in Chest X-ray Images
Saman Motamed, Patrik Rogalla, Farzad Khalvati
Successful training of convolutional neural networks (CNNs) requires a substantial amount of data. With small datasets networks generalize poorly. Data Augmentation techniques impr…
A Transfer Learning Approach for Automated Segmentation of Prostate Whole Gland and Transition Zone in Diffusion Weighted MRI
Saman Motamed, Isha Gujrathi, Dominik Deniffel +3
The segmentation of prostate whole gland and transition zone in Diffusion Weighted MRI (DWI) are the first step in designing computer-aided detection algorithms for prostate cancer…