论文标题
通过在第一卷积层最大化的分离指数对CNN的学习增强
Learning Enhancement of CNNs via Separation Index Maximizing at the First Convolutional Layer
论文作者
论文摘要
在本文中，提出了基于分离指数（SI）概念的直接增强学习算法（CNNS）。首先，将SI作为监督复杂性度量解释了其在更好地学习CNN的分类问题中的用法。然后，一种学习策略提出了通过最大化SI优化CNN的第一层，并通过反向传播算法对其他层进行训练，以了解更多的层。为了在第一层最大化SI，通过使用准最小平方误差技术来优化排名损失的变体。在几乎所有情况下，将这种学习策略应用于一些已知的CNN和数据集中，其增强影响。
In this paper, a straightforward enhancement learning algorithm based on Separation Index (SI) concept is proposed for Convolutional Neural Networks (CNNs). At first, the SI as a supervised complexity measure is explained its usage in better learning of CNNs for classification problems illustrate. Then, a learning strategy proposes through which the first layer of a CNN is optimized by maximizing the SI, and the further layers are trained through the backpropagation algorithm to learn further layers. In order to maximize the SI at the first layer, A variant of ranking loss is optimized by using the quasi least square error technique. Applying such a learning strategy to some known CNNs and datasets, its enhancement impact in almost all cases is demonstrated.
