Weaknesses: The idea of applying Shapley values for the understanding of deep neural networks is not new. Several works, such as Lundberg et al., 2017, have already discussed the theoretical motivation for using Shapley values as an attribution method to rank the importance of the input features. Lundberg et al., 2017 also proposed approximations like KernelSHAP and DeepSHAP, which are not compared to TMAB-Shapley. Besides this line of works, the idea of using Shapley values to rank the internal neurons has been proposed by the Stier et al., 2018 (cited) and Florin Leon, 2014 (not cited) in the context of pruning. Finally, Ancona et al., 2019 (not cited) proposed an approximation technique for Shapley values tailored for deep neural networks.