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Navid Kardan
Navid Kardan
Verified email at cs.ucf.edu
Title
Cited by
Cited by
Year
Advances in adversarial attacks and defenses in computer vision: A survey
N Akhtar, A Mian, N Kardan, M Shah
IEEE Access 9, 155161-155196, 2021
1932021
Openldn: Learning to discover novel classes for open-world semi-supervised learning
MN Rizve, N Kardan, S Khan, F Shahbaz Khan, M Shah
European Conference on Computer Vision, 382-401, 2022
442022
Mitigating fooling with competitive overcomplete output layer neural networks
N Kardan, KO Stanley
2017 International Joint Conference on Neural Networks (IJCNN), 518-525, 2017
412017
Towards realistic semi-supervised learning
MN Rizve, N Kardan, M Shah
European Conference on Computer Vision, 437-455, 2022
342022
Fitted learning: Models with awareness of their limits
N Kardan, KO Stanley
arXiv preprint arXiv:1609.02226, 2016
172016
Towards consistent predictive confidence through fitted ensembles
N Kardan, A Sharma, KO Stanley
2021 International Joint Conference on Neural Networks (IJCNN), 1-9, 2021
92021
Self-Joint Supervised Learning
N Kardan, M Shah, M Hill
International Conference on Learning Representations, 2021
42021
CDFSL-V: Cross-Domain Few-Shot Learning for Videos
S Samarasinghe, MN Rizve, N Kardan, M Shah
Proceedings of the IEEE/CVF International Conference on Computer Vision …, 2023
32023
Dual Student Networks for Data-Free Model Stealing
J Beetham, N Kardan, AS Mian, M Shah
The Eleventh International Conference on Learning Representations, 2022
32022
Detecting Compromised Architecture/Weights of a Deep Model
J Beetham, N Kardan, A Mian, M Shah
2022 26th International Conference on Pattern Recognition (ICPR), 2843-2849, 2022
12022
A Novel Fuzzy Rule-Based Classification System Based on Classifier Selection Strategy
N Kardan, B Minaei-Bidgoli
2009 International Conference on Computational Intelligence and Software …, 2009
12009
Self-Supervision is Not All You Need: In Defense of Semi-Supervised Learning
R Gupta, MN Rizve, S Sirnam, N Kardan, M Shah
2023
Towards More Reliable Neural Network Learning Models
N Kardan
2019
OpenLDN: Learning to Discover Novel Classes for Open-World Semi-Supervised Learning (Supplementary Material)
MN Rizve, N Kardan, S Khan, F Shahbaz
Towards Realistic Semi-Supervised Learning Supplementary Materials
MN Rizve, N Kardan, M Shah
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