图 书 馆
用于分层表示的可扩展无监督学习的卷积深度信念网络Convolutional Deep Belief Networks for Scalable Unsupervised Learning of Hierarchical Representations |
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| 课程网址: | http://videolectures.net/icml09_lee_cdb/ |
| 主讲教师: | Honglak Lee |
| 开课单位: | 密歇根大学 |
| 开课时间: | 2009-08-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | There has been much interest in unsupervised learning of hierarchical generative models such as deep belief networks. Scaling such models to full-sized, high-dimensional images remains a difficult problem. To address this problem, we present the convolutional deep belief network, a hierarchical generative model which scales to realistic image sizes. This model is translation-invariant and supports efficient bottom-up and top-down probabilistic inference. Key to our approach is probabilistic max-pooling, a novel technique which shrinks the representations of higher layers in a probabilistically sound way. Our experiments show that the algorithm learns useful high-level visual features, such as object parts, from unlabeled images of objects and natural scenes. We demonstrate excellent performance on several visual recognition tasks and show that our model can perform hierarchical (bottom-up and top-down) inference over full-sized images. |
| 关 键 词: | 成层级生模型; 概率最大化池; 高维度图像 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2019-04-23：lxf |
| 阅读次数: | 54 |