no code implementations • 7 Nov 2023 • Ruchit Rawal, Mariya Toneva
Possessing a wide variety of invariances may be a key reason for the recent successes of large language models, and our framework can shed light on the types of invariances that are retained by or emerge in new models.
2 code implementations • 10 Sep 2023 • Gaurav Kumar Nayak, Inder Khatri, Shubham Randive, Ruchit Rawal, Anirban Chakraborty
With the increasing deployment of deep neural networks in safety-critical applications such as self-driving cars, medical imaging, anomaly detection, etc., adversarial robustness has become a crucial concern in the reliability of these networks in real-world scenarios.
no code implementations • 12 Jul 2023 • Gabriele Merlin, Vedant Nanda, Ruchit Rawal, Mariya Toneva
The pretrain-finetune paradigm usually improves downstream performance over training a model from scratch on the same task, becoming commonplace across many areas of machine learning.
1 code implementation • 3 Nov 2022 • Gaurav Kumar Nayak, Ruchit Rawal, Inder Khatri, Anirban Chakraborty
These methods rely on the generation of adversarial samples in every episode of training, which further adds a computational burden.
1 code implementation • 3 Nov 2022 • Gaurav Kumar Nayak, Inder Khatri, Ruchit Rawal, Anirban Chakraborty
At test time, WNR combined with trained regenerator network is prepended to the black box network, resulting in a high boost in adversarial accuracy.
no code implementations • 17 Oct 2022 • Gaurav Kumar Nayak, Ruchit Rawal, Anirban Chakraborty
Existing works use this technique to provably secure a pretrained non-robust model by training a custom denoiser network on entire training data.
no code implementations • 5 May 2022 • Gaurav Kumar Nayak, Ruchit Rawal, Rohit Lal, Himanshu Patil, Anirban Chakraborty
We, therefore, propose a holistic approach for quantifying adversarial vulnerability of a sample by combining these different perspectives, i. e., degree of model's reliance on high-frequency features and the (conventional) sample-distance to the decision boundary.
no code implementations • 4 Apr 2022 • Gaurav Kumar Nayak, Ruchit Rawal, Anirban Chakraborty
Deep models are highly susceptible to adversarial attacks.
1 code implementation • 9 Nov 2021 • Chaitra Jambigi, Ruchit Rawal, Anirban Chakraborty
Learning modality invariant features is central to the problem of Visible-Thermal cross-modal Person Reidentification (VT-ReID), where query and gallery images come from different modalities.
1 code implementation • 26 Apr 2020 • Ruchit Rawal, Prabhu Pradhan
The utility of aerial imagery (Satellite, Drones) has become an invaluable information source for cross-disciplinary applications, especially for crisis management.