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Pretrained large-scale vision-language models like CLIP have exhibited strong generalization over unseen tasks.
A meta-learning framework for generalized zero-shot learning
Vinay Kumar Verma, Dhanajit Brahma, and Piyush Rai · 1909
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Learning generative visual models from few training examples: An incremental bayesian approach tested on 101 object categories
Li Fei-Fei, Rob Fergus, and Pietro Perona · 2004
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Caltech-256 object category dataset
Gregory Griffin, Alex Holub, and Pietro Perona · 2007
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Automated flower classification over a large number of classes
Maria-Elena Nilsback and Andrew Zisserman · 2008
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Imagenet: A large-scale hierarchical image database
Jia Deng, Wei Dong, Richard Socher, Li-Jia Li, Kai Li, and Li Fei-Fei · 2009
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Learning multiple layers of features from tiny images
Alex Krizhevsky, Geoffrey Hinton, et al · 2009
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Learning to detect unseen object classes by between-class attribute transfer
Christoph H. Lampert, Hannes Nickisch, and Stefan Harmeling · 2009
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Zero-shot learning with semantic output codes
Mark Palatucci, Dean Pomerleau, Geoffrey E Hinton, and Tom M Mitchell · 2009
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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
Adam Coates, Andrew Ng, and Honglak Lee · 2011
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Cats and dogs
Omkar M Parkhi, Andrea Vedaldi, Andrew Zisserman, and CV Jawahar · 2012
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Devise: A deep visual-semantic embedding model
Andrea Frome, Greg S Corrado, Jon Shlens, Samy Bengio, Jeff Dean, Marc Aurelio Ranzato, and Tomas Mikolov · 2013
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3d object representations for fine-grained categorization
Jonathan Krause, Michael Stark, Jia Deng, and Li Fei-Fei · 2013
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Fine-grained visual classification of aircraft
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi · 2013
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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 · 2013
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Food-101–mining discriminative components with random forests
Lukas Bossard, Matthieu Guillaumin, and Luc Van Gool · 2014
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Describing textures in the wild
M. Cimpoi, S. Maji, I. Kokkinos, S. Mohamed, , and A. Vedaldi · 2014
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Evaluation of output embeddings for fine-grained image classification
Zeynep Akata, Scott E. Reed, Daniel Walter, Honglak Lee, and Bernt Schiele · 2015
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The limitations of deep learning in adversarial settings
Nicolas Papernot, Patrick D. McDaniel, Somesh Jha, Matt Fredrikson, Z. Berkay Celik, and Ananthram Swami · 2015
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An embarrassingly simple approach to zero-shot learning
Bernardino Romera-Paredes and Philip H. S. Torr · 2015
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Deepfool: a simple and accurate method to fool deep neural networks, 2016
Seyed-Mohsen Moosavi-Dezfooli, Alhussein Fawzi, and Pascal Frossard · 2016
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Towards evaluating the robustness of neural networks
Nicholas Carlini and David A. Wagner · 2017
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Eurosat: A novel dataset and deep learning benchmark for land use and land cover classification, 2017
Patrick Helber, Benjamin Bischke, Andreas Dengel, and Damian Borth · 2017
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Adversarial examples in the physical world
Alexey Kurakin, Ian J. Goodfellow, and Samy Bengio · 2017
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Obfuscated gradients give a false sense of security: Circumventing defenses to adversarial examples
Anish Athalye, Nicholas Carlini, and David Wagner · 2018
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Boosting adversarial attacks with momentum
Yinpeng Dong, Fangzhou Liao, Tianyu Pang, Hang Su, Jun Zhu, Xiaolin Hu, and Jianguo Li · 2018
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Towards deep learning models resistant to adversarial attacks
Aleksander Madry, Aleksandar Makelov, Ludwig Schmidt, Dimitris Tsipras, and Adrian Vladu · 2018
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Representation learning with contrastive predictive coding
Aaron van den Oord, Yazhe Li, and Oriol Vinyals · 2018
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Is robustness the cost of accuracy?–a comprehensive study on the robustness of 18 deep image classification models
Dong Su, Huan Zhang, Hongge Chen, Jinfeng Yi, Pin-Yu Chen, and Yupeng Gao · 2018
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Zero-shot learning from adversarial feature residual to compact visual feature
Bo Liu, Qiulei Dong, and Zhanyi Hu · 2020
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Multitask learning strengthens adversarial robustness
Chengzhi Mao, Amogh Gupta, Vikram Nitin, Baishakhi Ray, Shuran Song, Junfeng Yang, and Carl Vondrick · 2020
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Bag of tricks for adversarial training, 2020
Tianyu Pang, Xiao Yang, Yinpeng Dong, Hang Su, and Jun Zhu · 2020
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Overfitting in adversarially robust deep learning, 2020
Leslie Rice, Eric Wong, and J. Zico Kolter · 2020
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Do adversarially robust imagenet models transfer better?
Hadi Salman, Andrew Ilyas, Logan Engstrom, Ashish Kapoor, and Aleksander Madry · 2020
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A deep dive into adversarial robustness in zero-shot learning
Mehmet Kerim Yucel, Ramazan Gokberk Cinbis, and Pinar Duygulu · 2020
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Rotation equivariant cnns for digital pathology
Bastiaan S Veeling, Jasper Linmans, Jim Winkens, Taco Cohen, and Max Welling · 2018
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Feature generating networks for zero-shot learning
Yongqin Xian, Tobias Lorenz, Bernt Schiele, and Zeynep Akata · 2018
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Stacked semantics-guided attention model for fine-grained zero-shot learning
Yunlong Yu, Zhong Ji, Yanwei Fu, Jichang Guo, Yanwei Pang, and Zhongfei (Mark) Zhang · 2018
Cited alongside, same era.
Generative dual adversarial network for generalized zero-shot learning
He Huang, Changhu Wang, Philip S. Yu, and Chang-Dong Wang · 2019
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Attribute attention for semantic disambiguation in zero-shot learning
Yang Liu, Jishun Guo, Deng Cai, and Xiaofei He · 2019
Cited alongside, same era.
Metric learning for adversarial robustness
Chengzhi Mao, Ziyuan Zhong, Junfeng Yang, Carl Vondrick, and Baishakhi Ray · 2019
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Dual adversarial semantics-consistent network for generalized zero-shot learning
Jian Ni, Shanghang Zhang, and Haiyong Xie · 2019
Cited alongside, same era.
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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 Le, Yun-Hsuan Sung, Zhen Li, and Tom Duerig · 2021
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Adversarial attacks are reversible with natural supervision
Chengzhi Mao, Mia Chiquier, Hao Wang, Junfeng Yang, and Carl Vondrick · 2021
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On the relationship between generalization and robustness to adversarial examples
Anibal Pedraza, Oscar Deniz, and Gloria Bueno · 2021
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Combined scaling for zero-shot transfer learning
Hieu Pham, Zihang Dai, Golnaz Ghiasi, Hanxiao Liu, Adams Wei Yu, Minh-Thang Luong, Mingxing Tan, and Quoc V Le · 2021
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Learning transferable visual models from natural language supervision
Alec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh, Gabriel Goh, Sandhini Agarwal, Girish Sastry, Amanda Askell, Pamela Mishkin, Jack Clark, et al · 2021
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Do as i can, not as i say: Grounding language in robotic affordances
Michael Ahn, Anthony Brohan, Noah Brown, Yevgen Chebotar, Omar Cortes, Byron David, Chelsea Finn, Keerthana Gopalakrishnan, Karol Hausman, Alex Herzog, et al · 2022
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Exploring visual prompts for adapting large-scale models
H Bahng, A Jahanian, S Sankaranarayanan, and P Isola · 2022
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Visual prompting via image inpainting
Amir Bar, Yossi Gandelsman, Trevor Darrell, Amir Globerson, and Alexei A. Efros · 2022
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Efficient transfer learning for visual tasks via continuous optimization of prompts
Jonathan Conder, Josephine Jefferson, Khurram Jawed, Alireza Nejati, Mark Sagar, et al · 2022
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Menglin Jia, Luming Tang, Bor-Chun Chen, Claire Cardie, Serge Belongie, Bharath Hariharan, and Ser-Nam Lim · 2022
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Using multiple self-supervised tasks improves model robustness
Matthew Lawhon, Chengzhi Mao, and Junfeng Yang · 2022
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Landscape learning for neural network inversion
Ruoshi Liu, Chengzhi Mao, Purva Tendulkar, Hao Wang, and Carl Vondrick · 2022
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Robust perception through equivariance, 2022
Chengzhi Mao, Lingyu Zhang, Abhishek Joshi, Junfeng Yang, Hao Wang, and Carl Vondrick · 2022
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Hierarchical text-conditional image generation with clip latents
Aditya Ramesh, Prafulla Dhariwal, Alex Nichol, Casey Chu, and Mark Chen · 2022
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Fine-tuning image transformers using learnable memory
Mark Sandler, Andrey Zhmoginov, Max Vladymyrov, and Andrew Jackson · 2022
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Test-time prompt tuning for zero-shot generalization in vision-language models, 2022
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Transferring adversarial robustness through robust representation matching
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Robust fine-tuning of zero-shot models
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