论文标题
在自动编码器的机理框架上
On a Mechanism Framework of Autoencoders
论文作者
论文摘要
本文提出了关于自动编码器机制的理论框架。在编码器部分中，在降低维度的主要用途下，我们研究了其两个基本属性：徒图和数据分离。给出了满足上述两个属性中两者的编码器的一般构造方法。对自动编码器的概括机制进行了建模。基于上面的理论框架，我们解释了变异自动编码器，降解自动编码器和线性单位自动编码器的一些实验结果，重点是解释通过编码器的数据较低维度表示。而且，通过自动编码器恢复图像的机理是很自然的，可以通过这些解释来理解。与PCA和决策树相比，分别证明了（广义）自动编码器对降低和分类的优势。卷积神经网络和随机加权的神经网络也通过此框架解释。
This paper proposes a theoretical framework on the mechanism of autoencoders. To the encoder part, under the main use of dimensionality reduction, we investigate its two fundamental properties: bijective maps and data disentangling. The general construction methods of an encoder that satisfies either or both of the above two properties are given. The generalization mechanism of autoencoders is modeled. Based on the theoretical framework above, we explain some experimental results of variational autoencoders, denoising autoencoders, and linear-unit autoencoders, with emphasis on the interpretation of the lower-dimensional representation of data via encoders; and the mechanism of image restoration through autoencoders is natural to be understood by those explanations. Compared to PCA and decision trees, the advantages of (generalized) autoencoders on dimensionality reduction and classification are demonstrated, respectively. Convolutional neural networks and randomly weighted neural networks are also interpreted by this framework.
