23 citations · 43 across the 8 of their papers we have counts for
4 papers · 1 filter
Negative Data Augmentation
Abhishek Sinha, Kumar Ayush, Jiaming Song +3
Data augmentation is often used to enlarge datasets with synthetic samples generated in accordance with the underlying data distribution. To enable a wider range of augmentations,…
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…
cFineGAN: Unsupervised multi-conditional fine-grained image generation
Gunjan Aggarwal, Abhishek Sinha
We propose an unsupervised multi-conditional image generation pipeline: cFineGAN, that can generate an image conditioned on two input images such that the generated image preserves…
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…