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Label smoothing is an effective regularization tool for deep neural networks (DNNs), which generates soft labels by applying a weighted average between the uniform distribution and the hard label.
J. Shu, Q. Xie, L. Yi, Q. Zhao, S. Zhou, Z. Xu, and D. Meng, “Meta-weight-net: Learning an explicit mapping for sample weighting,” in Adv. Neural Inform. Process. Syst. , 2019, pp. 1919–1930
1930
Earlier work this paper cites.
J. S. Duncan and T. Birkholzer, “Reinforcement of linear structure using parametrized relaxation labeling,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 14, no. 5, pp. 502–515, 1992
1992
Earlier work this paper cites.
M.-E. Nilsback and A. Zisserman, “Automated flower classification over a large number of classes,” in 2008 Sixth Indian Conference on Computer Vision, Graphics & Image Processing , 2008, pp. 722–729
2008
Earlier work this paper cites.
L. v. d. Maaten and G. Hinton, “Visualizing data using t-sne,” Journal of machine learning research , pp. 2579–2605, 2008
2008
Earlier work this paper cites.
A. Krizhevsky and G. Hinton, “Learning multiple layers of features from tiny images,” University of Toronto, Toronto, Ontario, Tech. Rep. 0, 2009
2009
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2009, pp. 248–255
2009
Earlier work this paper cites.
M. Everingham, L. Van Gool, C. K. Williams, J. Winn, and A. Zisserman, “The pascal visual object classes (voc) challenge,” Int. J. Comput. Vis. , vol. 88, no. 2, pp. 303–338, 2010
2010
Earlier work this paper cites.
C. Wah, S. Branson, P. Welinder, P. Perona, and S. Belongie, “The Caltech-UCSD Birds-200-2011 Dataset,” California Institute of Technology, Tech. Rep. CNS-TR-2011-001, 2011
2011
Earlier work this paper cites.
J. Krause, M. Stark, J. Deng, and L. Fei-Fei, “3d object representations for fine-grained categorization,” in Int. Conf. Comput. Vis. Worksh. , 2013, pp. 554–561
2013
Earlier work this paper cites.
S. Maji, J. Kannala, E. Rahtu, M. Blaschko, and A. Vedaldi, “A database for fine-grained aircraft recognition,” in IEEE Conf. Comput. Vis. Pattern Recog. Worksh. , June 2013
2013
Earlier work this paper cites.
K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” in Int. Conf. Learn. Represent. , 2015
2015
Earlier work this paper cites.
S. E. Reed, H. Lee, D. Anguelov, C. Szegedy, D. Erhan, and A. Rabinovich, “Training deep neural networks on noisy labels with bootstrapping,” in Int. Conf. Learn. Represent. Worksh. , 2015
2015
Earlier work this paper cites.
G. Hinton, O. Vinyals, and J. Dean, “Distilling the knowledge in a neural network,” in Adv. Neural Inform. Process. Syst. Worksh. , 2015
2015
Earlier work this paper cites.
T. Liu and D. Tao, “Classification with noisy labels by importance reweighting,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 38, no. 3, pp. 447–461, 2015
2015
Earlier work this paper cites.
A. Iscen, G. Tolias, P. Gosselin, and H. Jégou, “A comparison of dense region detectors for image search and fine-grained classification,” IEEE Trans. Image Process. , vol. 24, no. 8, pp. 2369–2381, 2015
2015
Earlier work this paper cites.
T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang, “Learning from massive noisy labeled data for image classification,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2015, pp. 2691–2699
2015
Earlier work this paper cites.
I. J. Goodfellow, J. Shlens, and C. Szegedy, “Explaining and harnessing adversarial examples,” in Int. Conf. Learn. Represent. , 2015
2015
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2016, pp. 770–778
2016
Earlier work this paper cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2016, pp. 2818–2826
2016
Earlier work this paper cites.
L. Xie, J. Wang, Z. Wei, M. Wang, and Q. Tian, “Disturblabel: Regularizing cnn on the loss layer,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2016, pp. 4753–4762
2016
Earlier work this paper cites.
Y. Zhang, X. Wei, J. Wu, J. Cai, J. Lu, V. Nguyen, and M. N. Do, “Weakly supervised fine-grained categorization with part-based image representation,” IEEE Trans. Image Process. , vol. 25, no. 4, pp. 1713–1725, 2016
2016
Earlier work this paper cites.
X. Shu, J. Tang, G.-J. Qi, Z. Li, Y.-G. Jiang, and S. Yan, “Image classification with tailored fine-grained dictionaries,” IEEE Trans. Circuit Syst. Video Technol. , vol. 28, no. 2, pp. 454–467, 2016
2016
Earlier work this paper cites.
J. Redmon, S. Divvala, R. Girshick, and A. Farhadi, “You only look once: Unified, real-time object detection,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2016, pp. 779–788
2016
Earlier work this paper cites.
W. Liu, D. Anguelov, D. Erhan, C. Szegedy, S. Reed, C.-Y. Fu, and A. C. Berg, “Ssd: Single shot multibox detector,” in Eur. Conf. Comput. Vis. , 2016, pp. 21–37
2016
Earlier work this paper cites.
G. Huang, Z. Liu, L. Van Der Maaten, and K. Q. Weinberger, “Densely connected convolutional networks,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2017, pp. 4700–4708
2017
Earlier work this paper cites.
S. Xie, R. Girshick, P. Dollár, Z. Tu, and K. He, “Aggregated residual transformations for deep neural networks,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2017, pp. 1492–1500
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
A. Paszke, S. Gross, S. Chintala, G. Chanan, E. Yang, Z. DeVito, Z. Lin, A. Desmaison, L. Antiga, and A. Lerer, “Automatic differentiation in pytorch,” in Adv. Neural Inform. Process. Syst. Worksh. , 2017
2017
Cited alongside, same era.
C. Zhang, C. Liang, L. Li, J. Liu, Q. Huang, and Q. Tian, “Fine-grained image classification via low-rank sparse coding with general and class-specific codebooks,” IEEE Trans. Neural Netw. Learn Syst. , vol. 28, no. 7, pp. 1550–1559, 2017
2017
Cited alongside, same era.
C. Zhang, S. Bengio, M. Hardt, B. Recht, and O. Vinyals, “Understanding deep learning requires rethinking generalization,” in Int. Conf. Learn. Represent. , 2017
2017
Cited alongside, same era.
A. Kurakin, I. J. Goodfellow, and S. Bengio, “Adversarial machine learning at scale,” in Int. Conf. Learn. Represent. , 2017
2017
Cited alongside, same era.
Z. Peng, Z. Li, J. Zhang, Y. Li, G.-J. Qi, and J. Tang, “Few-shot image recognition with knowledge transfer,” in Int. Conf. Comput. Vis. , 2019, pp. 441–449
2019
Later among the works it cites.
J. Yao, J. Wang, I. W. Tsang, Y. Zhang, J. Sun, C. Zhang, and R. Zhang, “Deep learning from noisy image labels with quality embedding,” IEEE Trans. Image Process. , vol. 28, no. 4, pp. 1909–1922, 2019
2019
Later among the works it cites.
J. Han, P. Luo, and X. Wang, “Deep self-learning from noisy labels,” in Int. Conf. Comput. Vis. , 2019, pp. 5138–5147
2019
Later among the works it cites.
Y. Wang, X. Ma, Z. Chen, Y. Luo, J. Yi, and J. Bailey, “Symmetric cross entropy for robust learning with noisy labels,” in Int. Conf. Comput. Vis. , 2019, pp. 322–330
2019
Later among the works it cites.
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S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 39, no. 6, pp. 1137–1149, 2017
2017
Cited alongside, same era.
C. Guo, G. Pleiss, Y. Sun, and K. Q. Weinberger, “On calibration of modern neural networks,” in Int. Conf. Mech. Learn. , 2017, pp. 1321–1330
2017
Cited alongside, same era.
J. Hu, L. Shen, and G. Sun, “Squeeze-and-excitation networks,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2018, pp. 7132–7141
2018
Cited alongside, same era.
M. Sandler, A. G. Howard, M. Zhu, A. Zhmoginov, and L. Chen, “Mobilenetv2: Inverted residuals and linear bottlenecks,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2018, pp. 4510–4520
2018
Cited alongside, same era.
H. Zhang, M. Cisse, Y. N. Dauphin, and D. Lopez-Paz, “mixup: Beyond empirical risk minimization,” in Int. Conf. Learn. Represent. , 2018
2018
Cited alongside, same era.
G. Ghiasi, T.-Y. Lin, and Q. V. Le, “Dropblock: A regularization method for convolutional networks,” in Adv. Neural Inform. Process. Syst. , 2018, pp. 10 727–10 737
2018
Cited alongside, same era.
A. Dubey, O. Gupta, P. Guo, R. Raskar, R. Farrell, and N. Naik, “Pairwise confusion for fine-grained visual classification,” in Eur. Conf. Comput. Vis. , 2018, pp. 70–86
2018
Cited alongside, same era.
T. Furlanello, Z. C. Lipton, M. Tschannen, L. Itti, and A. Anandkumar, “Born-again neural networks,” in Int. Conf. Mech. Learn. , 2018, pp. 1602–1611
2018
Cited alongside, same era.
2019
Later among the works it cites.
E. Arazo, D. Ortego, P. Albert, N. O’Connor, and K. Mcguinness, “Unsupervised label noise modeling and loss correction,” in Int. Conf. Mech. Learn. , 2019, pp. 312–321
2019
Later among the works it cites.
M. Fang, T. Zhou, J. Yin, Y. Wang, and D. Tao, “Data subset selection with imperfect multiple labels,” IEEE Trans. Neural Netw. Learn Syst. , vol. 30, no. 7, pp. 2212–2221, 2019
2019
Later among the works it cites.
K. Yi and J. Wu, “Probabilistic end-to-end noise correction for learning with noisy labels,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2019, pp. 7017–7025
2019
Later among the works it cites.
R. Müller, S. Kornblith, and G. E. Hinton, “When does label smoothing help?” in Adv. Neural Inform. Process. Syst. , 2019, pp. 4696–4705
2019
Later among the works it cites.
W. Shi, Y. Gong, X. Tao, D. Cheng, and N. Zheng, “Fine-grained image classification using modified dcnns trained by cascaded softmax and generalized large-margin losses,” IEEE Trans. Neural Netw. Learn Syst. , vol. 30, no. 3, pp. 683–694, 2019
2019
Later among the works it cites.
J. C. Peterson, R. M. Battleday, T. L. Griffiths, and O. Russakovsky, “Human uncertainty makes classification more robust,” in Int. Conf. Comput. Vis. , 2019, pp. 9616–9625
2019
Later among the works it cites.
F. Sun, T. Kong, W. Huang, C. Tan, B. Fang, and H. Liu, “Feature pyramid reconfiguration with consistent loss for object detection,” IEEE Trans. Image Process. , vol. 28, no. 10, pp. 5041–5051, 2019
2019
Later among the works it cites.
Z. Tian, C. Shen, H. Chen, and T. He, “Fcos: Fully convolutional one-stage object detection,” in Int. Conf. Comput. Vis. , 2019, pp. 9627–9636
2019
Later among the works it cites.
S.-H. Gao, M.-M. Cheng, K. Zhao, X.-Y. Zhang, M.-H. Yang, and P. Torr, “Res2net: A new multi-scale backbone architecture,” IEEE Trans. Pattern Anal. Mach. Intell. , vol. 43, no. 2, pp. 652–662, 2020
2020
Closest in time.
G.-J. Qi, “Loss-sensitive generative adversarial networks on lipschitz densities,” Int. J. Comput. Vis. , vol. 128, no. 5, pp. 1118–1140, 2020
2020
Closest in time.
H. Zhao, J. Jia, and V. Koltun, “Exploring self-attention for image recognition,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020, pp. 10 076–10 085
2020
Closest in time.
C. Li, C. Liu, L. Duan, P. Gao, and K. Zheng, “Reconstruction regularized deep metric learning for multi-label image classification,” IEEE Trans. Neural Netw. Learn Syst. , vol. 31, no. 7, pp. 2294–2303, 2020
2020
Closest in time.
G.-J. Qi, L. Zhang, F. Lin, and X. Wang, “Learning generalized transformation equivariant representations via autoencoding transformations,” IEEE Trans. Pattern Anal. Mach. Intell. , 2020
2020
Closest in time.
X. Wang, D. Kihara, J. Luo, and G.-J. Qi, “Enaet: A self-trained framework for semi-supervised and supervised learning with ensemble transformations,” IEEE Trans. Image Process. , 2020
2020
Closest in time.
S. Ge, Z. Luo, C. Zhang, Y. Hua, and D. Tao, “Distilling channels for efficient deep tracking,” IEEE Trans. Image Process. , vol. 29, pp. 2610–2621, 2020
2020
Closest in time.
N. Wang, W. Zhou, Y. Song, C. Ma, and H. Li, “Real-time correlation tracking via joint model compression and transfer,” IEEE Trans. Image Process. , vol. 29, pp. 6123–6135, 2020
2020
Closest in time.
Y. Wei, C. Gong, S. Chen, T. Liu, J. Yang, and D. Tao, “Harnessing side information for classification under label noise,” IEEE Trans. Neural Netw. Learn Syst. , vol. 31, no. 9, pp. 3178–3192, 2020
2020
Closest in time.
L. Yuan, F. E. Tay, G. Li, T. Wang, and J. Feng, “Revisiting knowledge distillation via label smoothing regularization,” in IEEE Conf. Comput. Vis. Pattern Recog. , 2020, pp. 3903–3911
2020
Closest in time.
S.-M. Hu, D. Liang, G.-Y. Yang, G.-W. Yang, and W.-Y. Zhou, “Jittor: a novel deep learning framework with meta-operators and unified graph execution,” Science China Information Sciences , vol. 63, no. 12, pp. 1–21, 2020
2020
Closest in time.
H. Zheng, J. Fu, Z. Zha, J. Luo, and T. Mei, “Learning rich part hierarchies with progressive attention networks for fine-grained image recognition,” IEEE Trans. Image Process. , vol. 29, pp. 476–488, 2020
2020
Closest in time.
F. Fang, L. Li, H. Zhu, and J. Lim, “Combining faster r-cnn and model-driven clustering for elongated object detection,” IEEE Trans. Image Process. , vol. 29, pp. 2052–2065, 2020
2020
Closest in time.