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学习卷积特征层次的视觉识别Learning Convolutional Feature Hierarchies for Visual Recognition |
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| 课程网址: | http://videolectures.net/nips2010_boureau_lcf/ |
| 主讲教师: | Y-Lan Boureau |
| 开课单位: | 纽约大学 |
| 开课时间: | 2011-03-25 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We propose an unsupervised method for learning multi-stage hierarchies of sparse convolutional features. While sparse coding has become an increasingly popular method for learning visual features, it is most often trained at the patch level. Applying the resulting filters convolutionally results in highly redundant codes because overlapping patches are encoded in isolation. By training convolutionally over large image windows, our method reduces the redudancy between feature vectors at neighboring locations and improves the efficiency of the overall representation. In addition to a linear decoder that reconstructs the image from sparse features, our method trains an efficient feed-forward encoder that predicts quasi-sparse features from the input. While patch-based training rarely produces anything but oriented edge detectors, we show that convolutional training produces highly diverse filters, including center-surround filters, corner detectors, cross detectors, and oriented grating detectors. We show that using these filters in multi-stage convolutional network architecture improves performance on a number of visual recognition and detection tasks. |
| 关 键 词: | 计算机科学; 机器学习; 无监督学习 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2020-06-02：毛岱琦(课程编辑志愿者) |
| 阅读次数: | 46 |