5 citations · 7 across the 4 of their papers we have counts for
8 papers
Designing Counterfactual Generators using Deep Model Inversion
Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Deepta Rajan +3
Explanation techniques that synthesize small, interpretable changes to a given image while producing desired changes in the model prediction have become popular for introspecting b…
On the Design of Deep Priors for Unsupervised Audio Restoration
Vivek Sivaraman Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias
Unsupervised deep learning methods for solving audio restoration problems extensively rely on carefully tailored neural architectures that carry strong inductive biases for definin…
Loss Estimators Improve Model Generalization
Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Deepta Rajan +1
With increased interest in adopting AI methods for clinical diagnosis, a vital step towards safe deployment of such tools is to ensure that the models not only produce accurate pre…
Using Deep Image Priors to Generate Counterfactual Explanations
Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Andreas Spanias
Through the use of carefully tailored convolutional neural network architectures, a deep image prior (DIP) can be used to obtain pre-images from latent representation encodings. Th…
Accurate and Robust Feature Importance Estimation under Distribution Shifts
Jayaraman J. Thiagarajan, Vivek Narayanaswamy, Rushil Anirudh +2
With increasing reliance on the outcomes of black-box models in critical applications, post-hoc explainability tools that do not require access to the model internals are often use…
Unsupervised Audio Source Separation using Generative Priors
Vivek Narayanaswamy, Jayaraman J. Thiagarajan, Rushil Anirudh +1
State-of-the-art under-determined audio source separation systems rely on supervised end-end training of carefully tailored neural network architectures operating either in the tim…