22 citations · 44 across the 18 of their papers we have counts for
6 papers · 1 filter
Efficient and Effective Augmentation Strategy for Adversarial Training
Sravanti Addepalli, Samyak Jain, R. Venkatesh Babu
Adversarial training of Deep Neural Networks is known to be significantly more data-hungry when compared to standard training. Furthermore, complex data augmentations such as AutoA…
Towards Efficient and Effective Self-Supervised Learning of Visual Representations
Sravanti Addepalli, Kaushal Bhogale, Priyam Dey +1
Self-supervision has emerged as a propitious method for visual representation learning after the recent paradigm shift from handcrafted pretext tasks to instance-similarity based a…
Scaling Adversarial Training to Large Perturbation Bounds
Sravanti Addepalli, Samyak Jain, Gaurang Sriramanan +1
The vulnerability of Deep Neural Networks to Adversarial Attacks has fuelled research towards building robust models. While most Adversarial Training algorithms aim at defending at…
Learning an Invertible Output Mapping Can Mitigate Simplicity Bias in Neural Networks
Sravanti Addepalli, Anshul Nasery, R. Venkatesh Babu +2
Deep Neural Networks are known to be brittle to even minor distribution shifts compared to the training distribution. While one line of work has demonstrated that Simplicity Bias (…
DAFT: Distilling Adversarially Fine-tuned Models for Better OOD Generalization
Anshul Nasery, Sravanti Addepalli, Praneeth Netrapalli +1
We consider the problem of OOD generalization, where the goal is to train a model that performs well on test distributions that are different from the training distribution. Deep l…
Towards Data-Free Model Stealing in a Hard Label Setting
Sunandini Sanyal, Sravanti Addepalli, R. Venkatesh Babu
Machine learning models deployed as a service (MLaaS) are susceptible to model stealing attacks, where an adversary attempts to steal the model within a restricted access framework…