23 citations · 34 across the 4 of their papers we have counts for
10 papers
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…
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…
ShapeVis: High-dimensional Data Visualization at Scale
Nupur Kumari, Siddarth R., Akash Rupela +2
We present ShapeVis, a scalable visualization technique for point cloud data inspired from topological data analysis. Our method captures the underlying geometric and topological s…
A Method for Computing Class-wise Universal Adversarial Perturbations
Tejus Gupta, Abhishek Sinha, Nupur Kumari +2
We present an algorithm for computing class-specific universal adversarial perturbations for deep neural networks. Such perturbations can induce misclassification in a large fracti…
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…