# Unsupervised Domain Adaptation via Stacked Convolutional Autoencoder +
###### Abstract
In recent decades, remarkable progress has been made in the use of feature learning methods based on shallow structure and deep neural networks which have learned how to transfer representations across domains and have performed well in unsupervised domain adaptation. Typical shallow learning methods, such as transfer component analysis [13], aim to reduce domain divergence in new feature space using a kernel function. In comparison to shallow structure methods, deep neural networks have been shown to be more effective by separating the explanatory factors behind different domains [14; 15]. Recently, mainstream deep neural networks, such as the convolutional neural network (CNN) [16], the recurrent neural network (RNN) [17], the generative adversarial network (GAN) [18], and Autoencoder [19], have been used to learn more discriminative representations for unsupervised domain adaptation and have performed well in reducing domain divergence. Among unsupervised domain adaptation methods that are based on deep neural networks, the autoencoder-based method has achieved superior performance with respect to the no label requirement and fast convergence speed. For example, the stacked denoising autoencoder (SDA) method [20] aims to learn higher-level representations from all available domains to train a classifier that performs classification on new-featured spaces. Similarly, to address the issue of high computational cost in SDA, a marginalized stacked denoising autoencoder method [19] has been proposed based on matrix computation, which is as effective as SDA in representation learning for domain adaptation and has been shown to be more efficient. In light of the development of these methods, [PERSON] et al. proposed an unsupervised domain adaptation method based on non-linear representation learning [21], which introduced non-linear coding by kernelization into SDA to enable the extraction of deep features. While it is possible to explore different domains and learn transferable and discriminative representations using unsupervised domain adaptation methods based on autoencoders, most current approaches depend on use of the classical structure of autoencoders or integration of regularization terms into the objective function [22; 23; 24]. For improved understanding of feature representation learning, here, a method is proposed to achieve representation learning based on a stacked convolutional sparse autoencoder for unsupervised domain adaptation, which can capture more transferable and distinguishable features by layer mapping of the raw data and unsupervised domain adaptation. Firstly, we utilize the reconstruction independent component analysis (RICA) algorithm [4] with whitening to pre-process the original data in both source and target domains, where \"whitening\" refers to a transformation of the original data \(x\) to \(x_{whitened}\), and the covariance matrix of \(x_{whitened}\) is the identity matrix. A stacked sparse autoencoder is then introduced to extract features to alleviate domain discrepancy. Secondly, based on the new feature space learned by the first component, convolution and pooling are applied to maintain local relevance. Finally, we stack two convolutional sparse autoencoders to achieve more abstract and transferable representation learning. Compared to other state-of-the-art methods, experimental results obtained confirm the effectiveness of our proposed framework for unsupervised domain adaptation. In summary, this paper makes the following main contributions:  
* We explicitly propose a new framework of unsupervised domain adaptation based on a stacked convolutional sparse autoencoder (short for SCSA). There is an obvious distinction between this method and the original method [2; 14], which relies on applying the classical structure of the autoencoder to learn representations or integratation of the regularization term into the objective function. * Our proposed SCSA has two main components in each layer.