publication . Preprint . Conference object . 2017

Efficient and Invariant Convolutional Neural Networks for Dense Prediction

Hongyang Gao; Shuiwang Ji;
Open Access English
  • Published: 24 Nov 2017
Abstract
Convolutional neural networks have shown great success on feature extraction from raw input data such as images. Although convolutional neural networks are invariant to translations on the inputs, they are not invariant to other transformations, including rotation and flip. Recent attempts have been made to incorporate more invariance in image recognition applications, but they are not applicable to dense prediction tasks, such as image segmentation. In this paper, we propose a set of methods based on kernel rotation and flip to enable rotation and flip invariance in convolutional neural networks. The kernel rotation can be achieved on kernels of 3 $\times$ 3, w...
Persistent Identifiers
Subjects
ACM Computing Classification System: ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Artificial intelligence, business.industry, business, Convolutional neural network, Computer science, Image segmentation, Invariant (mathematics), Convolution, Invariant (physics), Kernel (linear algebra), Feature extraction, Pattern recognition
16 references, page 1 of 2

[1] I. Goodfellow, Y. Bengio, and A. Courville, Deep Learning. The MIT Press, 2016.

[2] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, “Gradient-based learning applied to document recognition,” Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, November 1998.

[3] A. Krizhevsky, I. Sutskever, and G. E. Hinton, “Imagenet classification with deep convolutional neural networks,” in Advances in neural information processing systems, 2012, pp. 1097-1105.

[4] J. Long, E. Shelhamer, and T. Darrell, “Fully convolutional networks for semantic segmentation,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, 2015, pp. 3431-3440.

[5] T. Cohen and M. Welling, “Group equivariant convolutional networks,” in Proceedings of The 33rd International Conference on Machine Learning, 2016, pp. 2990-2999.

[6] M. A. Islam, N. Bruce, and Y. Wang, “Dense image labeling using deep convolutional neural networks,” in Computer and Robot Vision (CRV), 2016 13th Conference on. IEEE, 2016, pp. 16-23.

[7] K. Simonyan and A. Zisserman, “Very deep convolutional networks for large-scale image recognition,” Proceedings of the International Conference on Learning Representations, 2015.

[8] S. Dieleman, K. W. Willett, and J. Dambre, “Rotation-invariant convolutional neural networks for galaxy morphology prediction.” Monthly notices of the royal astronomical society., vol. 450, no. 2, pp. 1441- 1459, 2015.

[9] S. Dieleman, J. D. Fauw, and K. Kavukcuoglu, “Exploiting cyclic symmetry in convolutional neural networks,” in Proceedings of The 33rd International Conference on Machine Learning, 2016, pp. 1889-1898. [OpenAIRE]

[10] D. Eigen, C. Puhrsch, and R. Fergus, “Depth map prediction from a single image using a multi-scale deep network,” in Advances in neural information processing systems, 2014, pp. 2366-2374. [OpenAIRE]

[11] I. Laina, C. Rupprecht, V. Belagiannis, F. Tombari, and N. Navab, “Deeper depth prediction with fully convolutional residual networks,” in 2016 Fourth International Conference on 3D Vision. IEEE, 2016, pp. 239-248. [OpenAIRE]

[12] O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation.” in International Conference on Medical Image Computing and Computer-Assisted Intervention, 2015, pp. 234-241.

[13] H. Noh, S. Hong, and B. Han, “Learning deconvolution network for semantic segmentation.” in IEEE International Conference on Computer Vision, 2015.

[14] I. Goodfellow, D. Warde-farley, M. Mirza, A. Courville, and Y. Bengio, “Maxout networks,” in Proceedings of the 30th International Conference on Machine Learning (ICML-13), 2013, pp. 1319-1327.

[15] A. Fakhry, T. Zeng, and S. Ji, “Residual deconvolutional networks for brain electron microscopy image segmentation,” IEEE Transactions on Medical Imaging, 2016.

16 references, page 1 of 2
Any information missing or wrong?Report an Issue