To summarize the results obtained with synthetic EIS data, we first noted that the DNN training improved the DRT and impedance recoveries compared to RR for all cases except the hook model. Using the single ZARC model, we showed that i) the DRT and impedance errors were negligibly affected by the number of collocation points; ii) the DRT and impedance recoveries became less precise as the level of experimental noise increased; and iii) the regularization parameter for RR did not affect the DNN performance. We also showed that this new DNN architecture can recover various impedances, including those with negative peaks. Furthermore, the DNN-DRT model performed as well or even better than the deep-DRT model and DRTtools in the various cases investigated. Lastly, we confirmed the conclusion drawn for each synthetic case using ten synthetic experiments.