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Within the realm of computer vision, self-supervised learning (SSL) pertains to training pre-trained image encoders utilizing a substantial quantity of unlabeled images.
Improved baselines with momentum contrastive learning,
X. Chen, H. Fan, R. Girshick, K. He, · 2003
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Adam: A method for stochastic optimization,
D. P. Kingma, J. Ba, · 2014
Earlier work this paper cites.
U-net: Convolutional networks for biomedical image segmentation,
O. Ronneberger, P. Fischer, T. Brox, · 2015
Earlier work this paper cites.
Spatial transformer networks,
M. Jaderberg, K. Simonyan, A. Zisserman, et al., · 2015
Earlier work this paper cites.
Unsupervised visual representation learning by context prediction,
C. Doersch, A. Gupta, A. A. Efros, · 2015
Earlier work this paper cites.
Deep residual learning for image recognition,
K. He, X. Zhang, S. Ren, J. Sun, · 2016
Earlier work this paper cites.
Unsupervised learning of visual representations by solving jigsaw puzzles,
M. Noroozi, P. Favaro, · 2016
Earlier work this paper cites.
Colorful image colorization,
R. Zhang, P. Isola, A. A. Efros, · 2016
Earlier work this paper cites.
Badnets: Identifying vulnerabilities in the machine learning model supply chain,
T. Gu, B. Dolan-Gavitt, S. Garg, · 2017
Earlier work this paper cites.
Hiding images in plain sight: Deep steganography,
S. Baluja, · 2017
Earlier work this paper cites.
Neural trojans,
Y. Liu, Y. Xie, A. Srivastava, · 2017
Earlier work this paper cites.
Targeted backdoor attacks on deep learning systems using data poisoning,
X. Chen, C. Liu, B. Li, K. Lu, D. Song, · 2017
Earlier work this paper cites.
Learning deep representations by mutual information estimation and maximization,
R. D. Hjelm, A. Fedorov, S. Lavoie-Marchildon, K. Grewal, P. Bachman, A. Trischler, Y. Bengio, · 2018
Earlier work this paper cites.
Trojaning attack on neural networks,
Y. Liu, S. Ma, Y. Aafer, W.-C. Lee, J. Zhai, W. Wang, X. Zhang, · 2018
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Hidden: Hiding data with deep networks,
J. Zhu, R. Kaplan, J. Johnson, L. Fei-Fei, · 2018
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The unreasonable effectiveness of deep features as a perceptual metric,
R. Zhang, P. Isola, A. A. Efros, E. Shechtman, O. Wang, · 2018
Cited alongside, same era.
Boosting self-supervised learning via knowledge transfer,
M. Noroozi, A. Vinjimoor, P. Favaro, H. Pirsiavash, · 2018
Cited alongside, same era.
Unsupervised representation learning by predicting image rotations,
N. Komodakis, S. Gidaris, · 2018
Cited alongside, same era.
Strip: A defence against trojan attacks on deep neural networks,
Y. Gao, C. Xu, D. Wang, S. Chen, D. C. Ranasinghe, S. Nepal, · 2019
Cited alongside, same era.
Abs: Scanning neural networks for back-doors by artificial brain stimulation,
Y. Liu, W.-C. Lee, G. Tao, S. Ma, Y. Aafer, X. Zhang, · 2019
Cited alongside, same era.
Bootstrap your own latent-a new approach to self-supervised learning,
J.-B. Grill, F. Strub, F. Altché, C. Tallec, P. Richemond, E. Buchatskaya, C. Doersch, B. Avila Pires, Z. Guo, M. Gheshlaghi Azar, et al., · 2020
Learning transferable visual models from natural language supervision,
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al., · 2021
Later among the works it cites.
Invisible backdoor attack with sample-specific triggers,
Y. Li, Y. Li, B. Wu, L. Li, R. He, S. Lyu, · 2021
Later among the works it cites.
Backdoor scanning for deep neural networks through k-arm optimization,
G. Shen, Y. Liu, G. Tao, S. An, Q. Xu, S. Cheng, S. Ma, X. Zhang, · 2021
Later among the works it cites.
Backdoor attacks on self-supervised learning,
A. Saha, A. Tejankar, S. A. Koohpayegani, H. Pirsiavash, · 2022
Later among the works it cites.
Demystifying self-supervised trojan attacks,
C. Li, R. Pang, Z. Xi, T. Du, S. Ji, Y. Yao, T. Wang, · 2022
Later among the works it cites.
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Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning,
K. He, H. Fan, Y. Wu, S. Xie, R. Girshick, · 2020
Cited alongside, same era.
Stegastamp: Invisible hyperlinks in physical photographs,
M. Tancik, B. Mildenhall, R. Ng, · 2020
Cited alongside, same era.
Momentum contrast for unsupervised visual representation learning,
K. He, H. Fan, Y. Wu, S. Xie, R. Girshick, · 2020
Cited alongside, same era.
Big self-supervised models are strong semi-supervised learners,
T. Chen, S. Kornblith, K. Swersky, M. Norouzi, G. E. Hinton, · 2020
Cited alongside, same era.
Input-aware dynamic backdoor attack,
T. A. Nguyen, A. Tran, · 2020
Cited alongside, same era.
Backdooring and poisoning neural networks with image-scaling attacks,
E. Quiring, K. Rieck, · 2020
Cited alongside, same era.
C. Luo, Q. Lin, W. Xie, B. Wu, J. Xie, L. Shen, · 2022
Later among the works it cites.
Poison ink: Robust and invisible backdoor attack,
J. Zhang, C. Dongdong, Q. Huang, J. Liao, W. Zhang, H. Feng, G. Hua, N. Yu, · 2022
Later among the works it cites.
An invisible black-box backdoor attack through frequency domain,
T. Wang, Y. Yao, F. Xu, S. An, H. Tong, T. Wang, · 2022
Later among the works it cites.
{ \{ PoisonedEncoder
H. Liu, J. Jia, N. Z. Gong, · 2022
Later among the works it cites.
Better trigger inversion optimization in backdoor scanning,
G. Tao, G. Shen, Y. Liu, S. An, Q. Xu, S. Ma, P. Li, X. Zhang, · 2022
Later among the works it cites.
Detecting backdoors in pre-trained encoders,
S. Feng, G. Tao, S. Cheng, G. Shen, X. Xu, Y. Liu, K. Zhang, S. Ma, X. Zhang, · 2023
Closest in time.
Ssl-cleanse: Trojan detection and mitigation in self-supervised learning,
M. Zheng, J. Xue, X. Chen, L. Jiang, Q. Lou, · 2023
Closest in time.
Defending against patch-based backdoor attacks on self-supervised learning,
A. Tejankar, M. Sanjabi, Q. Wang, S. Wang, H. Firooz, H. Pirsiavash, L. Tan, · 2023
Closest in time.
Latent backdoor attacks on deep neural networks,
Y. Yao, H. Li, H. Zheng, B. Y. Zhao, · 2055
Closest in time.
Badencoder: Backdoor attacks to pre-trained encoders in self-supervised learning,
J. Jia, Y. Liu, N. Z. Gong, · 2059
Closest in time.