32 citations · 33 across the 3 of their papers we have counts for
4 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…
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…
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…