publication . Conference object . Other literature type . Preprint . 2017

Between-Class Learning for Image Classification

Tokozume, Yuji; Ushiku, Yoshitaka; Harada, Tatsuya;
Open Access
  • Published: 28 Nov 2017
  • Publisher: IEEE
Abstract
Comment: 11 pages, 8 figures, published as a conference paper at CVPR 2018
Subjects
free text keywords: Computer Science - Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
30 references, page 1 of 2

[1] O. Abdel-Hamid, A.-r. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu. Convolutional neural networks for speech recognition. IEEE/ACM TASLP, 22(10):1533-1545, 2014. 1

[2] S. An, F. Boussaid, and M. Bennamoun. How can deep rectifier networks achieve linear separability and preserve distances? In ICML, 2015. 3 [OpenAIRE]

[3] Authors. Learning from between-class examples for deep sound recognition. ICLR, 2018. https://openreview. net/forum?id=B1Gi6LeRZ. 1, 2, 3, 4, 7

[4] Y. Bengio, J. Louradour, R. Collobert, and J. Weston. Curriculum learning. In ICML, 2009. 6 [OpenAIRE]

[5] P. Burt and E. Adelson. The laplacian pyramid as a compact image code. TCOM, 31(4):532-540, 1983. 4 [OpenAIRE]

[6] R. Collobert, S. Bengio, and J. Marie´thoz. Torch: a modular machine learning software library. Technical report, Idiap, 2002. 5, 6

[7] W. Dai, C. Dai, S. Qu, J. Li, and S. Das. Very deep convolutional neural networks for raw waveforms. In ICASSP, 2017. 4

[8] R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of eugenics, 7(2):179-188, 1936. 3, 10

[9] X. Gastaldi. Shake-shake regularization. In ICLR Workshop, 2017. 2, 5, 6, 10, 11

[10] K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In ICCV, 2015. 11

[11] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016. 1

[12] J. Hu, L. Shen, and G. Sun. Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507, 2017. 1

[13] G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten. Densely connected convolutional networks. 5, 6, 10, 11

[14] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015. 1, 11 [OpenAIRE]

[15] D. Kingma and J. Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 1

30 references, page 1 of 2
Abstract
Comment: 11 pages, 8 figures, published as a conference paper at CVPR 2018
Subjects
free text keywords: Computer Science - Learning, Computer Science - Computer Vision and Pattern Recognition, Statistics - Machine Learning
30 references, page 1 of 2

[1] O. Abdel-Hamid, A.-r. Mohamed, H. Jiang, L. Deng, G. Penn, and D. Yu. Convolutional neural networks for speech recognition. IEEE/ACM TASLP, 22(10):1533-1545, 2014. 1

[2] S. An, F. Boussaid, and M. Bennamoun. How can deep rectifier networks achieve linear separability and preserve distances? In ICML, 2015. 3 [OpenAIRE]

[3] Authors. Learning from between-class examples for deep sound recognition. ICLR, 2018. https://openreview. net/forum?id=B1Gi6LeRZ. 1, 2, 3, 4, 7

[4] Y. Bengio, J. Louradour, R. Collobert, and J. Weston. Curriculum learning. In ICML, 2009. 6 [OpenAIRE]

[5] P. Burt and E. Adelson. The laplacian pyramid as a compact image code. TCOM, 31(4):532-540, 1983. 4 [OpenAIRE]

[6] R. Collobert, S. Bengio, and J. Marie´thoz. Torch: a modular machine learning software library. Technical report, Idiap, 2002. 5, 6

[7] W. Dai, C. Dai, S. Qu, J. Li, and S. Das. Very deep convolutional neural networks for raw waveforms. In ICASSP, 2017. 4

[8] R. A. Fisher. The use of multiple measurements in taxonomic problems. Annals of eugenics, 7(2):179-188, 1936. 3, 10

[9] X. Gastaldi. Shake-shake regularization. In ICLR Workshop, 2017. 2, 5, 6, 10, 11

[10] K. He, X. Zhang, S. Ren, and J. Sun. Delving deep into rectifiers: Surpassing human-level performance on imagenet classification. In ICCV, 2015. 11

[11] K. He, X. Zhang, S. Ren, and J. Sun. Deep residual learning for image recognition. In CVPR, 2016. 1

[12] J. Hu, L. Shen, and G. Sun. Squeeze-and-excitation networks. arXiv preprint arXiv:1709.01507, 2017. 1

[13] G. Huang, Z. Liu, K. Q. Weinberger, and L. van der Maaten. Densely connected convolutional networks. 5, 6, 10, 11

[14] S. Ioffe and C. Szegedy. Batch normalization: Accelerating deep network training by reducing internal covariate shift. In ICML, 2015. 1, 11 [OpenAIRE]

[15] D. Kingma and J. Ba. Adam: A method for stochastic optimization. In ICLR, 2015. 1

30 references, page 1 of 2
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