图 书 馆
生成和条件图像模型Generative and Discriminative Image Models |
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| 课程网址: | http://videolectures.net/nipsworkshops09_winn_gdim/ |
| 主讲教师: | John Winn |
| 开课单位: | 微软公司 |
| 开课时间: | 2010-03-26 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | Creating a good probabilistic model for images is a challenging task, due to the large variability in natural images. For general photographs, an ideal generative model would have to cope with scene layout, occlusion, variability in object appearance, variability in object position and 3D rotation and illumination effects like shading and shadows. The formidable challenges in creating such a model have led many researchers to pursue discriminative models, which instead use image features that are largely invariant to many of these sources of variability. In this talk, I will compare both approaches and describe some strengths and weaknesses of each and suggest some directions in which the best aspects of both can be combined. |
| 关 键 词: | 概率模型; 自然图像; 生成模型 |
| 课程来源: | 视频讲座网 |
| 最后编审: | 2021-08-28：zyk |
| 阅读次数: | 68 |