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
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...
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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
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