A Mueller matrix (MM) provides a comprehensive representation of the polarization properties of a complex medium and encodes very rich information on the macro- and microstructural features. Histopathological features can be characterized by polarization parameters derived from MM. However, a MM must be derived from at least four Stokes vectors corresponding to four different incident polarization states, which makes the qualities of MM very sensitive to small changes in the imaging system or the sample during the exposures, such as fluctuations in illumination light and co-registration of polarization component images. In this work, we use a deep learning approach to retrieve MM-based specific polarimetry basis parameters (PBPs) from a snapshot Stokes vector. This data post-processing method is capable of eliminating errors introduced by multi-exposure, as well as reducing the imaging time and hardware complexity. It shows the potential for accurate MM imaging on dynamic samples or in unstable environments. The translation model is designed based on generative adversarial network with customized loss functions. The effectiveness of the approach was demonstrated on liver and breast tissue slices and blood smears. Finally, we evaluated the performance by quantitative similarity assessment methods in both pixel and image levels.