图 书 馆
深度神经决策森林Deep Neural Decision Forests |
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| 课程网址: | http://videolectures.net/iccv2015_kontschieder_decision_forests/ |
| 主讲教师: | Peter Kontschieder |
| 开课单位: | 微软研究院、剑桥大学、微软研究院 |
| 开课时间: | 2016-02-10 |
| 课程语种: | 英语 |
| 中文简介: |  |
| 课程简介: | We present Deep Neural Decision Forests – a novel approach that unifies classification trees with the representation learning functionality known from deep convolutional networks, by training them in an end-to-end manner. To combine these two worlds, we introduce a stochastic and differentiable decision tree model, which steers the representation learning usually conducted in the initial layers of a (deep) convolutional network. Our model differs from conventional deep networks because a decision forest provides the final predictions and it differs from conventional decision forests since we propose a principled, joint and global optimization of split and leaf node parameters. We show experimental results on benchmark machine learning datasets like MNIST and ImageNet and find on par or superior results when compared to state-of-the-art deep models. Most remarkably, we obtain Top5-Errors of only 7.84%/6.38% on ImageNet validation data when integrating our forests in a single-crop, single/seven model GoogLeNet architecture, respectively. Thus, even without any form of training data set augmentation we are improving on the 6.67% error obtained by the best GoogLeNet architecture (7 models, 144 crops). |
| 关 键 词: | 深度神经; 决策模型; 决策森林 |
| 课程来源: | 视频讲座网 |
| 数据采集: | 2023-03-22：chenxin01 |
| 最后编审: | 2023-05-17：liyy |
| 阅读次数: | 23 |