Motivated by the successful application in computer vision (Kan et al., 2015), we construct the architecture of BTDNNs with one encoder f e , and two decoders, g s and g t shown in Figure 1, which can transform an input example to the source domain and the target domain respectively.
2 the encoder f c tries to map an input example x into the latent feature representation z, which is common to both the source and target domains as follows: The decoder g s attempts to map the latent representation to the source domain, and the decoder g t attempts to map the latent representation to the target domain as follows: where s e (•) and s d (•) are the element-wise nonlinear activation function, e.g., sigmoid or tanh function, W c and b c are the parameters for encoder f c , W s and b s are the parameters for decoder g s , W t and b t are the parameters for decoder g t .