Multipath (MP) degradation of GNSSS measurements is the main source of error in urban areas. Robust mitigation of this error source is still a challenge for standalone low-cost GNSS receivers. The complexity associated with the development of MP degradation models requires the use of advanced methods such as Deep Learning (DL). However, DL based mitigation methods tend to be hard to deploy due to a general lack of trust in their prediction due to their “black-box” behavior. This work tackles the notion of interpretability and generalization of MP degradation models obtained using Auto-Encoders (AE). We demonstrate the ability of AE to generate interpretable representations and to generalize to unseen situations.