23 citations · 69 across the 28 of their papers we have counts for
5 papers · 1 filter
Data InStance Prior (DISP) in Generative Adversarial Networks
Puneet Mangla, Nupur Kumari, Mayank Singh +2
Recent advances in generative adversarial networks (GANs) have shown remarkable progress in generating high-quality images. However, this gain in performance depends on the availab…
LT-GAN: Self-Supervised GAN with Latent Transformation Detection
Parth Patel, Nupur Kumari, Mayank Singh +1
Generative Adversarial Networks (GANs) coupled with self-supervised tasks have shown promising results in unconditional and semi-supervised image generation. We propose a self-supe…
Augmented Convolutional LSTMs for Generation of High-Resolution Climate Change Projections
Nidhin Harilal, Udit Bhatia, Mayank Singh
Projection of changes in extreme indices of climate variables such as temperature and precipitation are critical to assess the potential impacts of climate change on human-made and…
On the Benefits of Models with Perceptually-Aligned Gradients
Gunjan Aggarwal, Abhishek Sinha, Nupur Kumari +1
Adversarial robust models have been shown to learn more robust and interpretable features than standard trained models. As shown in [\cite{tsipras2018robustness}], such robust mode…
Attributional Robustness Training using Input-Gradient Spatial Alignment
Mayank Singh, Nupur Kumari, Puneet Mangla +3
Interpretability is an emerging area of research in trustworthy machine learning. Safe deployment of machine learning system mandates that the prediction and its explanation be rel…