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Matthew Inkawhich
Matthew Inkawhich
ECE Graduate Student, Duke University
Bestätigte E-Mail-Adresse bei duke.edu
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Zitiert von
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Perturbing across the feature hierarchy to improve standard and strict blackbox attack transferability
N Inkawhich, K Liang, B Wang, M Inkawhich, L Carin, Y Chen
Advances in Neural Information Processing Systems 33, 20791-20801, 2020
762020
Bridging a gap in SAR-ATR: Training on fully synthetic and testing on measured data
N Inkawhich, MJ Inkawhich, EK Davis, UK Majumder, E Tripp, C Capraro, ...
IEEE Journal of Selected Topics in Applied Earth Observations and Remote …, 2021
592021
Adversarial attacks for optical flow-based action recognition classifiers
N Inkawhich, M Inkawhich, Y Chen, H Li
arXiv preprint arXiv:1811.11875, 2018
362018
Snooping attacks on deep reinforcement learning
M Inkawhich, Y Chen, H Li
arXiv preprint arXiv:1905.11832, 2019
312019
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
282021
Self-trained proposal networks for the open world
M Inkawhich, N Inkawhich, H Li, Y Chen
arXiv preprint arXiv:2208.11050, 2022
32022
Tunable Hybrid Proposal Networks for the Open World
M Inkawhich, N Inkawhich, H Li, Y Chen
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024
22024
OSR-ViT: A Simple and Modular Framework for Open-Set Object Detection and Discovery
M Inkawhich, N Inkawhich, H Yang, J Zhang, R Linderman, Y Chen
arXiv preprint arXiv:2404.10865, 2024
2024
The Untapped Potential of Off-the-Shelf Convolutional Neural Networks
M Inkawhich, N Inkawhich, E Davis, H Li, Y Chen
Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2022
2022
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