论文标题
深层卷积神经网络使用ECOC
Deep Convolutional Neural Network Ensembles using ECOC
论文作者
论文摘要
深度神经网络在许多应用程序中增强了决策系统的性能，包括图像理解，并通过构建合奏可以实现进一步的收益。但是，设计深网集合通常不是很有益，因为训练网络所需的时间非常高，或者获得的性能增益不是很重要。在本文中，我们分析了纠正输出编码（ECOC）框架的错误，以用作深网的合奏技术，并提出不同的设计策略来解决准确的复杂性权衡。我们在引入的ECOC设计与最新的集合技术（例如合奏平均和梯度增强决策树）之间进行了广泛的比较研究。此外，我们提出了一种组合技术，该技术被证明可以在所有人之间取得最高的分类性能。
Deep neural networks have enhanced the performance of decision making systems in many applications including image understanding, and further gains can be achieved by constructing ensembles. However, designing an ensemble of deep networks is often not very beneficial since the time needed to train the networks is very high or the performance gain obtained is not very significant. In this paper, we analyse error correcting output coding (ECOC) framework to be used as an ensemble technique for deep networks and propose different design strategies to address the accuracy-complexity trade-off. We carry out an extensive comparative study between the introduced ECOC designs and the state-of-the-art ensemble techniques such as ensemble averaging and gradient boosting decision trees. Furthermore, we propose a combinatory technique which is shown to achieve the highest classification performance amongst all.
