Name
Abstract | ||
|---|---|---|
Deep learning has made significant improvements at many image processing tasks in recent years, such as image classification, object recognition and object detection. Convolutional neural networks (CNN), which is a popular deep learning architecture designed to process data in multiple array form, show great success to almost all detection u0026 recognition problems and computer vision tasks. However, the number of parameters in a CNN is too high such that the computers require more energy and larger memory size. In order to solve this problem, we propose a novel energy efficient model Binary Weight and Hadamard-transformed Image Network (BWHIN), which is a combination of Binary Weight Network (BWN) and Hadamard-transformed Image Network (HIN). It is observed that energy efficiency is achieved with a slight sacrifice at classification accuracy. Among all energy efficient networks, our novel ensemble model outperforms other energy efficient models. |
Year | Venue | Field |
|---|---|---|
2018 | arXiv: Computer Vision and Pattern Recognition | Object detection,Pattern recognition,Computer science,Efficient energy use,Convolutional neural network,Image processing,Artificial intelligence,Deep learning,Contextual image classification,Artificial neural network,Hadamard transform |
DocType | Volume | Citations |
Journal | abs/1805.05421 | 1 |
PageRank | References | Authors |
0.38 | 10 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| T. Ceren Deveci | 1 | 1 | 0.38 |
| Serdar Çakir | 2 | 10 | 1.91 |
| A. Enis Çetin | 3 | 871 | 118.56 |