| Feature space perturbations yield more transferable adversarial examples N Inkawhich, W Wen, HH Li, Y Chen IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2019 | 203 | 2019 |
| DVERGE: diversifying vulnerabilities for enhanced robust generation of ensembles H Yang, J Zhang, H Dong, N Inkawhich, A Gardner, A Touchet, W Wilkes, ... Advances in Neural Information Processing Systems (NeurIPS), 2020 | 118 | 2020 |
| Transferable Perturbations of Deep Feature Distributions N Inkawhich, KJ Liang, L Carin, Y Chen International Conference on Learning Representations (ICLR), 2020 | 88 | 2020 |
| Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability N Inkawhich, KJ Liang, B Wang, M Inkawhich, L Carin, Y Chen Advances in Neural Information Processing Systems (NeurIPS), 2020 | 78 | 2020 |
| Bridging a Gap in SAR-ATR: Training on Fully Synthetic and Testing on Measured Data N Inkawhich, MJ Inkawhich, E Davis, U Majumder, E Tripp, CT Capraro, ... IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2021 | 72 | 2021 |
| NTIRE 2021 multi-modal aerial view object classification challenge J Liu, N Inkawhich, O Nina, R Timofte Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2021 | 42 | 2021 |
| Adversarial attacks for optical flow-based action recognition classifiers N Inkawhich, M Inkawhich, Y Chen, H Li arXiv preprint arXiv:1811.11875, 2018 | 36 | 2018 |
| Training SAR-ATR models for reliable operation in open-world environments NA Inkawhich, EK Davis, MJ Inkawhich, UK Majumder, Y Chen IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2021 | 31 | 2021 |
| Mixture outlier exposure: Towards out-of-distribution detection in fine-grained environments J Zhang, N Inkawhich, R Linderman, Y Chen, H Li Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2023 | 25 | 2023 |
| Fine-grained out-of-distribution detection with mixup outlier exposure J Zhang, N Inkawhich, Y Chen, H Li CoRR, 2021 | 23 | 2021 |
| Advanced Techniques for Robust SAR ATR: Mitigating Noise and Phase Errors N Inkawhich, E Davis, U Majumder, C Capraro, Y Chen International Radar Conference (RADAR), 2020 | 19 | 2020 |
| Finetuning torchvision models N Inkawhich Py-Torch tutorials, 2017 | 19 | 2017 |
| Improving out-of-distribution detection by learning from the deployment environment N Inkawhich, J Zhang, EK Davis, R Luley, Y Chen IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2022 | 15 | 2022 |
| Multi-modal aerial view object classification challenge results-PBVS 2023 S Low, O Nina, AD Sappa, E Blasch, N Inkawhich Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2023 | 13 | 2023 |
| A global model approach to robust few-shot SAR automatic target recognition N Inkawhich IEEE Geoscience and Remote Sensing Letters 20, 1-5, 2023 | 10 | 2023 |
| High-performance computing for automatic target recognition in synthetic aperture radar imagery U Majumder, E Christiansen, Q Wu, N Inkawhich, E Blasch, J Nehrbass Cyber Sensing 2017 10185, 76-83, 2017 | 9 | 2017 |
| Fine-grain inference on out-of-distribution data with hierarchical classification R Linderman, J Zhang, N Inkawhich, H Li, Y Chen Conference on Lifelong Learning Agents, 162-183, 2023 | 5 | 2023 |
| Adversarial attacks on foundational vision models N Inkawhich, G McDonald, R Luley arXiv preprint arXiv:2308.14597, 2023 | 5 | 2023 |
| Can Targeted Adversarial Examples Transfer When the Source and Target Models Have No Label Space Overlap? N Inkawhich, KJ Liang, J Zhang, H Yang, H Li, Y Chen Proceedings of the IEEE/CVF International Conference on Computer Vision, 41-50, 2021 | 5 | 2021 |
| Adversarial example generation N Inkawhich PyTorch, 2017 | 5 | 2017 |
Yiran ChenJohn Cocke Distinguished Professor of Electrical and Computer Engineering, Duke University在 duke.edu 的电子邮件经过验证
