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A large number of works aim to alleviate the impact of noise due to an underlying conventional assumption of the negative role of noise.
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2010
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2014
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I. J. Goodfellow, J. Pouget-Abadie, M. Mirza, B. Xu, D. Warde-Farley, S. Ozair, A. C. Courville, and Y. Bengio, “Generative adversarial nets,” in Advances in Neural Information Processing Systems 27: Annual Conference on Neural Information Processing Systems 2014, December 8-13 2014, Montreal, Quebec, Canada , 2014, pp. 2672–2680
2014
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2014
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O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei, “ImageNet Large Scale Visual Recognition Challenge,” International Journal of Computer Vision (IJCV) , vol. 115, no. 3, pp. 211–252, 2015
2015
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2015
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I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in 3rd International Conference on Learning Representations, ICLR 2015, San Diego, CA, USA, May 7-9, 2015, Conference Track Proceedings , 2015
2015
Cited alongside, same era.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in 2016 IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2016, Las Vegas, NV, USA, June 27-30, 2016 . IEEE Computer Society, 2016, pp. 770–778
2016
Cited alongside, same era.
W. Hu, T. Miyato, S. Tokui, E. Matsumoto, and M. Sugiyama, “Learning discrete representations via information maximizing self-augmented training,” in Proceedings of the 34th International Conference on Machine Learning, ICML 2017, Sydney, NSW, Australia, 6-11 August 2017 , vol. 70, 2017, pp. 1558–1567
2017
Cited alongside, same era.
A. A. Alemi, I. Fischer, J. V. Dillon, and K. Murphy, “Deep variational information bottleneck,” in 5th International Conference on Learning Representations, ICLR 2017, Toulon, France, April 24-26, 2017, Conference Track Proceedings , 2017
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2020
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A. Dosovitskiy, L. Beyer, A. Kolesnikov, D. Weissenborn, X. Zhai, T. Unterthiner, M. Dehghani, M. Minderer, G. Heigold, S. Gelly, J. Uszkoreit, and N. Houlsby, “An image is worth 16x16 words: Transformers for image recognition at scale,” in 9th International Conference on Learning Representations, ICLR 2021, Virtual Event, Austria, May 3-7, 2021 , 2021
2021
Later among the works it cites.
A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, G. Krueger, and I. Sutskever, “Learning transferable visual models from natural language supervision,” in Proceedings of the 38th International Conference on Machine Learning, ICML 2021, 18-24 July 2021, Virtual Event , vol. 139. PMLR, 2021, pp. 8748–8763
2021
Later among the works it cites.
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2017
Cited alongside, same era.
2017
Cited alongside, same era.
2018
Cited alongside, same era.
R. Müller, S. Kornblith, and G. E. Hinton, “When does label smoothing help?” in Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019 , 2019, pp. 4696–4705
2019
Cited alongside, same era.
E. D. Cubuk, B. Zoph, D. Mané, V. Vasudevan, and Q. V. Le, “Autoaugment: Learning augmentation strategies from data,” in IEEE Conference on Computer Vision and Pattern Recognition, CVPR 2019 . Computer Vision Foundation / IEEE, 2019, pp. 113–123
2019
Cited alongside, same era.
J. Su, D. V. Vargas, and K. Sakurai, “One pixel attack for fooling deep neural networks,” IEEE Trans. Evol. Comput. , vol. 23, no. 5, pp. 828–841, 2019
2019
Cited alongside, same era.
J. Ho, A. Jain, and P. Abbeel, “Denoising diffusion probabilistic models,” in Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, NeurIPS 2020, December 6-12, 2020, virtual , 2020
2020
Cited alongside, same era.
T. Chen, S. Kornblith, M. Norouzi, and G. E. Hinton, “A simple framework for contrastive learning of visual representations,” in Proceedings of the 37th International Conference on Machine Learning, ICML 2020 , vol. 119, 2020, pp. 1597–1607
2020
Cited alongside, same era.
G. Chen, C. Fan, J. Sun, and J. Xia, “Mean square exponential stability analysis for itô stochastic systems with aperiodic sampling and multiple time-delays,” IEEE Transactions on Automatic Control , vol. 67, no. 5, pp. 2473–2480, 2021
2021
Later among the works it cites.
X. Li, “Positive-incentive noise,” IEEE Transactions on Neural Networks and Learning Systems , pp. 1–7, 2022
2022
Later among the works it cites.
G. Li, G. Kang, X. Wang, Y. Wei, and Y. Yang, “Adversarially masking synthetic to mimic real: Adaptive noise injection for point cloud segmentation adaptation,” in Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) , June 2023, pp. 20 464–20 474
2023
Closest in time.
H. Qiu, M. Xia, Y. Zhang, Y. He, X. Wang, Y. Shan, and Z. Liu, “Freenoise: Tuning-free longer video diffusion via noise rescheduling,” in The Twelfth International Conference on Learning Representations , 2024
2024
Closest in time.
C. Shi, K. Huang, G. Lu, H. Liu, M. Zhu, N. Wang, and X. Gao, “On the analysis of gan-based image-to-image translation with gaussian noise injection,” in The Twelfth International Conference on Learning Representations , 2024
2024
Closest in time.
2024
Closest in time.
2025
Closest in time.
S. Huang, H. Zhang, and X. Li, “Enhance vision-language alignment with noise,” in Proceedings of the AAAI Conference on Artificial Intelligence , vol. 39, no. 16, 2025, pp. 17 449–17 457
2025
Closest in time.