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Sparse Convolutional Neural Networks
2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2015Deep neural networks have achieved remarkable performance in both image classification and object detection problems, at the cost of a large number of parameters and computational complexity. In this work, we show how to reduce the redundancy in these parameters using a sparse decomposition. Maximum sparsity is obtained by exploiting both inter-channel
Liu, Baoyuan +4 more
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Quadtree Convolutional Neural Networks
2018This paper presents a Quadtree Convolutional Neural Network (QCNN) for efficiently learning from image datasets representing sparse data such as handwriting, pen strokes, freehand sketches, etc. Instead of storing the sparse sketches in regular dense tensors, our method decomposes and represents the image as a linear quadtree that is only refined in ...
Pradeep Kumar Jayaraman +3 more
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A Survey on Convolution Neural Networks
2020 IEEE REGION 10 CONFERENCE (TENCON), 2020Major tools to implement any Artificial Intelligence and Machine Learning systems are Symbolic AI and Artificial Neural Network (ANN) AI. ANN has made a dramatic improvement in the versatile area of Machine Learning (ML). ANN is a gathering of vast number of weighted interconnected artificial neurons, initially invented with the inspiration of ...
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A novel graph convolutional feature based convolutional neural network for stock trend prediction
Information Sciences, 2021Zhensong Chen +2 more
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IFCNN: A general image fusion framework based on convolutional neural network
Information Fusion, 2020Yu Zhang, Yu Liu
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