| Mmd-reid: A simple but effective solution for visible-thermal person reid C Jambigi, R Rawal, A Chakraborty arXiv preprint arXiv:2111.05059, 2021 | 22 | 2021 |
| DAD: Data-free Adversarial Defense at Test Time GK Nayak, R Rawal, A Chakraborty Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022 | 12 | 2022 |
| Climate Adaptation: Reliably Predicting from Imbalanced Satellite Data R Rawal, P Pradhan The IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR …, 2020 | 3 | 2020 |
| What happens during finetuning of vision transformers: An invariance based investigation G Merlin, V Nanda, R Rawal, M Toneva Conference on Lifelong Learning Agents, 601-619, 2023 | 2 | 2023 |
| De-crop: Data-efficient certified robustness for pretrained classifiers GK Nayak, R Rawal, A Chakraborty Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 2 | 2023 |
| Holistic approach to measure sample-level adversarial vulnerability and its utility in building trustworthy systems GK Nayak, R Rawal, R Lal, H Patil, A Chakraborty Proceedings of the IEEE/CVF conference on computer vision and pattern …, 2022 | 2 | 2022 |
| Robust Few-Shot Learning Without Using Any Adversarial Samples GK Nayak, R Rawal, I Khatri, A Chakraborty IEEE Transactions on Neural Networks and Learning Systems, 2024 | 1 | 2024 |
| Data-free Defense of Black Box Models Against Adversarial Attacks GK Nayak, I Khatri, R Rawal, A Chakraborty Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2024 | 1 | 2024 |
| Generalizing across the (in) visible spectrum R Rawal, P Pradhan ICML 2020 (International Conference on Machine Learning) Workshops, XC'20 …, 2020 | 1 | 2020 |
| Cinepile: A long video question answering dataset and benchmark R Rawal, K Saifullah, R Basri, D Jacobs, G Somepalli, T Goldstein arXiv preprint arXiv:2405.08813, 2024 | | 2024 |
| Perturbed examples reveal invariances shared by language models R Rawal, M Toneva arXiv preprint arXiv:2311.04166, 2023 | | 2023 |
| DAD++: Improved Data-free Test Time Adversarial Defense GK Nayak, I Khatri, S Randive, R Rawal, A Chakraborty arXiv preprint arXiv:2309.05132, 2023 | | 2023 |
| Rendezvous between Robustness and Dataset Bias: An empirical study P Pradhan, R Rawal, G Kishan Conference on Neural Information Processing Systems (NeurIPS)'20 Workshop, 2020 | | 2020 |
| Supplementary for:“Data-free Defense of Black Box Models Against Adversarial Attacks” GK Nayak, I Khatri, R Rawal, A Chakraborty | | |
Anirban ChakrabortyIndian Institute of Science, Bangalore在 iisc.ac.in 的电子邮件经过验证
