  Domain adaptation aims to exploit the knowledge in source domain to promote
the learning tasks in target domain, which plays a critical role in real-world
applications. Recently, lots of deep learning approaches based on autoencoders
have achieved a significance performance in domain adaptation. However, most
existing methods focus on minimizing the distribution divergence by putting the
source and target data together to learn global feature representations, while
they do not consider the local relationship between instances in the same
category from different domains. To address this problem, we propose a novel
Semi-Supervised Representation Learning framework via Dual Autoencoders for
domain adaptation, named SSRLDA. More specifically, we extract richer feature
representations by learning the global and local feature representations
simultaneously using two novel autoencoders, which are referred to as
marginalized denoising autoencoder with adaptation distribution (MDAad) and
multi-class marginalized denoising autoencoder (MMDA) respectively. Meanwhile,
we make full use of label information to optimize feature representations.
Experimental results show that our proposed approach outperforms several
state-of-the-art baseline methods.
