4 papers
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…
Enforcing Linearity in DNN succours Robustness and Adversarial Image Generation
Anindya Sarkar, Nikhil Kumar Gupta, Raghu Iyengar
Recent studies on the adversarial vulnerability of neural networks have shown that models trained with the objective of minimizing an upper bound on the worst-case loss over all po…
ODE guided Neural Data Augmentation Techniques for Time Series Data and its Benefits on Robustness
Anindya Sarkar, Anirudh Sunder Raj, Raghu Sesha Iyengar
Exploring adversarial attack vectors and studying their effects on machine learning algorithms has been of interest to researchers. Deep neural networks working with time series da…