3.3k citations · 3.4k across the 6 of their papers we have counts for
7 papers
Modularity Trumps Invariance for Compositional Robustness
Ian Mason, Anirban Sarkar, Tomotake Sasaki +1
By default neural networks are not robust to changes in data distribution. This has been demonstrated with simple image corruptions, such as blurring or adding noise, degrading ima…
Deephys: Deep Electrophysiology, Debugging Neural Networks under Distribution Shifts
Anirban Sarkar, Matthew Groth, Ian Mason +2
Deep Neural Networks (DNNs) often fail in out-of-distribution scenarios. In this paper, we introduce a tool to visualize and understand such failures. We draw inspiration from conc…
Get Fooled for the Right Reason: Improving Adversarial Robustness through a Teacher-guided Curriculum Learning Approach
Anindya Sarkar, Anirban Sarkar, Sowrya Gali +1
Current SOTA adversarially robust models are mostly based on adversarial training (AT) and differ only by some regularizers either at inner maximization or outer minimization steps…
A Framework for Learning Ante-hoc Explainable Models via Concepts
Anirban Sarkar, Deepak Vijaykeerthy, Anindya Sarkar +1
Self-explaining deep models are designed to learn the latent concept-based explanations implicitly during training, which eliminates the requirement of any post-hoc explanation gen…
Enhanced Regularizers for Attributional Robustness
Anindya Sarkar, Anirban Sarkar, Vineeth N Balasubramanian
Deep neural networks are the default choice of learning models for computer vision tasks. Extensive work has been carried out in recent years on explaining deep models for vision t…
Neural Network Attributions: A Causal Perspective
Aditya Chattopadhyay, Piyushi Manupriya, Anirban Sarkar +1
We propose a new attribution method for neural networks developed using first principles of causality (to the best of our knowledge, the first such). The neural network architectur…
Grad-CAM++: Improved Visual Explanations for Deep Convolutional Networks
Aditya Chattopadhyay, Anirban Sarkar, Prantik Howlader +1
Over the last decade, Convolutional Neural Network (CNN) models have been highly successful in solving complex vision problems. However, these deep models are perceived as "black b…