  We propose a method for using synthetic data to help learning classifiers.
Synthetic data, even is generated based on real data, normally results in a
shift from the distribution of real data in feature space. To bridge the gap
between the real and synthetic data, and jointly learn from synthetic and real
data, this paper proposes a Multichannel Autoencoder(MCAE). We show that by
suing MCAE, it is possible to learn a better feature representation for
classification. To evaluate the proposed approach, we conduct experiments on
two types of datasets. Experimental results on two datasets validate the
efficiency of our MCAE model and our methodology of generating synthetic data.
