How robust are discriminatively trained zero-shot learning models?-Reference-Cited by-同舟云学术

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How robust are discriminatively trained zero-shot learning models?

Published:2022-03 Issue: Volume:119 Page:104392
ISSN:0262-8856
Container-title:Image and Vision Computing
language:en
Short-container-title:Image and Vision Computing

Author:

Yucel Mehmet Kerim^ORCID,Cinbis Ramazan Gokberk^ORCID,Duygulu Pinar

Publisher

Elsevier BV

Subject

Computer Vision and Pattern Recognition,Signal Processing

Reference63 articles.

1. Spatially transformed adversarial examples;Xiao,2018

2. One pixel attack for fooling deep neural networks;Su;IEEE Trans. Evolut. Computat.,2019

3. The limitations of deep learning in adversarial settings;Papernot,2016

4. Unrestricted adversarial examples via semantic manipulation;Bhattad,2020

5. Practical black-box attacks against machine learning;Papernot,2017

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1. A comprehensive review on zero-shot-learning techniques;Intelligent Decision Technologies;2024-06-07

2. A comprehensive review on zero-shot-learning techniques;Intelligent Decision Technologies;2024-04-17

3. A Modular System for Enhanced Robustness of Multimedia Understanding Networks via Deep Parametric Estimation;Proceedings of the ACM Multimedia Systems Conference 2024 on ZZZ;2024-04-15

4. Finding Waldo;Proceedings of the ACM Multimedia Systems Conference 2024 on ZZZ;2024-04-15

5. Feature decoupling and interaction network for defending against adversarial examples;Image and Vision Computing;2024-04

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