| idlg: Improved deep leakage from gradients B Zhao, KR Mopuri, H Bilen arXiv preprint arXiv:2001.02610, 2020 | 527 | 2020 |
| Dataset condensation with gradient matching B Zhao, KR Mopuri, H Bilen arXiv preprint arXiv:2006.05929, 2020 | 323 | 2020 |
| A taxonomy of deep convolutional neural nets for computer vision S Srinivas, RK Sarvadevabhatla, KR Mopuri, N Prabhu, SSS Kruthiventi, ... Frontiers in Robotics and AI 2, 36, 2016 | 281 | 2016 |
| Zero-shot knowledge distillation in deep networks GK Nayak, KR Mopuri, V Shaj, VB Radhakrishnan, A Chakraborty International Conference on Machine Learning, 4743-4751, 2019 | 250 | 2019 |
| Fast feature fool: A data independent approach to universal adversarial perturbations KR Mopuri, U Garg, RV Babu Proccedings of British Machine Vision Conference (BMVC), 2017 | 250 | 2017 |
| Generalizable data-free objective for crafting universal adversarial perturbations KR Mopuri, A Ganeshan, VB Radhakrishnan IEEE transactions on pattern analysis and machine intelligence, 2018 | 219 | 2018 |
| NAG: Network for Adversary Generation KR Mopuri, U Ojha, U Garg, RV Babu The IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2018 | 185 | 2018 |
| Ask, acquire, and attack: Data-free uap generation using class impressions KR Mopuri, PK Uppala, RV Babu Proceedings of the European Conference on Computer Vision (ECCV), 19-34, 2018 | 98 | 2018 |
| Object level deep feature pooling for compact image representation K Reddy Mopuri, R Venkatesh Babu Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2015 | 81 | 2015 |
| Cnn fixations: an unraveling approach to visualize the discriminative image regions KR Mopuri, U Garg, RV Babu IEEE Transactions on Image Processing 28 (5), 2116-2125, 2018 | 68 | 2018 |
| Gray-box adversarial training BS Vivek, KR Mopuri, RV Babu Proceedings of the European conference on computer vision (ECCV), 203-218, 2018 | 46 | 2018 |
| An introduction to deep convolutional neural nets for computer vision S Srinivas, RK Sarvadevabhatla, KR Mopuri, N Prabhu, SSS Kruthiventi, ... Deep learning for medical image analysis, 25-52, 2017 | 39 | 2017 |
| Early stuck pipe sign detection with depth-domain 3D convolutional neural network using actual drilling data N Tsuchihashi, R Wada, M Ozaki, T Inoue, KR Mopuri, H Bilen, ... SPE Journal 26 (02), 551-562, 2021 | 32 | 2021 |
| Bilen, H. idlg: Improved deep leakage from gradients B Zhao, KR Mopuri arXiv preprint arXiv:2001.02610, 2020 | 32 | 2020 |
| Early sign detection for the stuck pipe scenarios using unsupervised deep learning KR Mopuri, H Bilen, N Tsuchihashi, R Wada, T Inoue, K Kusanagi, ... Journal of Petroleum Science and Engineering 208, 109489, 2022 | 23 | 2022 |
| Effectiveness of arbitrary transfer sets for data-free knowledge distillation GK Nayak, KR Mopuri, A Chakraborty Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2021 | 21 | 2021 |
| Class balancing gan with a classifier in the loop H Rangwani, KR Mopuri, RV Babu Uncertainty in Artificial Intelligence, 1618-1627, 2021 | 18 | 2021 |
| Learning to retain while acquiring: combating distribution-shift in adversarial data-free knowledge distillation G Patel, KR Mopuri, Q Qiu Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 13 | 2023 |
| An unsupervised learning model for pipe stuck predictions using a long short-term memory autoencoder architecture Y Nakagawa, T Inoue, H Bilen, KR Mopuri, K Miyoshi, S Abe3, R Wada, ... SPE Asia Pacific Oil and Gas Conference and Exhibition, D011S009R006, 2021 | 9 | 2021 |
| Mining Data Impressions From Deep Models as Substitute for the Unavailable Training Data GK Nayak, KR Mopuri, S Jain, A Chakraborty IEEE Transactions on Pattern Analysis and Machine Intelligence 44 (11), 8465 …, 2021 | 8 | 2021 |
R. Venkatesh BabuCDS, Indian Institute of Science, Bangalore, IndiaDirección de correo verificada de iisc.ac.in
