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Deep Neural Networks (DNNs) are known to be susceptible to adversarial attacks.
ATZSL: defensive zero-shot recognition in the presence of adversaries
Xingxing Zhang, Shupeng Gui, Zhenfeng Zhu, Yao Zhao, and Ji Liu · 1910
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General n-dimensional rotations
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
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Caltech-256 object category dataset
Gregory Griffin, Alex Holub, and Pietro Perona · 2007
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Describing objects by their attributes
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Sun database: Large-scale scene recognition from abbey to zoo
Jianxiong Xiao, James Hays, Krista A Ehinger, Aude Oliva, and Antonio Torralba · 2010
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An analysis of single-layer networks in unsupervised feature learning
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Cats and dogs
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Attribute-based classification for zero-shot visual object categorization
Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling · 2014
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Microsoft COCO: common objects in context
Tsung-Yi Lin, Michael Maire, Serge J. Belongie, James Hays, Pietro Perona, Deva Ramanan, Piotr Dollár, and C. Lawrence Zitnick · 2014
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Intriguing properties of neural networks
Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian J. Goodfellow, and Rob Fergus · 2014
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Explaining and harnessing adversarial examples
Ian J. Goodfellow et al · 2015
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Imagenet large scale visual recognition challenge
Olga Russakovsky, Jia Deng, Hao Su, Jonathan Krause, Sanjeev Satheesh, Sean Ma, Zhiheng Huang, Andrej Karpathy, Aditya Khosla, Michael S. Bernstein, Alexander C. Berg, and Li Fei-Fei · 2015
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An empirical study and analysis of generalized zero-shot learning for object recognition in the wild
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Deep residual learning for image recognition
Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun · 2016
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Latent embeddings for zero-shot classification
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Towards evaluating the robustness of neural networks
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Prototypical networks for few-shot learning
Jake Snell, Kevin Swersky, and Richard S. Zemel · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
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Zero-shot detection via vision and language knowledge distillation
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Scaling up visual and vision-language representation learning with noisy text supervision
Chao Jia, Yinfei Yang, Ye Xia, Yi-Ting Chen, Zarana Parekh, Hieu Pham, Quoc V. Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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Learning transferable visual models from natural language supervision
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IEPT: instance-level and episode-level pretext tasks for few-shot learning
Manli Zhang, Jianhong Zhang, Zhiwu Lu, Tao Xiang, Mingyu Ding, and Songfang Huang · 2021
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Robust physical-world attacks on deep learning visual classification
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Towards deep learning models resistant to adversarial attacks
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Representation learning with contrastive predictive coding
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Stacked semantics-guided attention model for fine-grained zero-shot learning
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Meta-learning with differentiable closed-form solvers
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Metric learning for adversarial robustness
Chengzhi Mao, Ziyuan Zhong, Junfeng Yang, Carl Vondrick, and Baishakhi Ray · 2019
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Fooling thermal infrared pedestrian detectors in real world using small bulbs
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Pre-trained adversarial perturbations
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A light recipe to train robust vision transformers
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Improving adversarially robust few-shot image classification with generalizable representations
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Rethinking the number of shots in robust model-agnostic meta-learning
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Language-driven semantic segmentation
Boyi Li, Kilian Q. Weinberger, Serge J. Belongie, Vladlen Koltun, and René Ranftl · 2022
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How robust are discriminatively trained zero-shot learning models?
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Towards adversarial attack on vision-language pre-training models
Jiaming Zhang, Qi Yi, and Jitao Sang · 2022
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Enhancing adversarial robustness for deep metric learning
Mo Zhou and Vishal M Patel · 2022
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Semantically consistent visual representation for adversarial robustness
Huafeng Kuang, Hong Liu, Yongjian Wu, and Rongrong Ji · 2023
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Understanding zero-shot adversarial robustness for large-scale models
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