A Computing Information Measures Our goal is to compute the Entropy for a neuron, A

Then, the probability for each activation is determined by the number of activations that share the same discrete value. Kraskov et al. (2004), which provides a tight lower bound to the mutual information, especially in Kraskov et al. (2004) consider the popular interpretation of mutual information Popular recent estimators take a neural approach for estimating MI and consider an alternate interpretation of MI as the dependence between two random variables, i.e., Details for network architectures and training regimes are given in Table 3. All images in the training and evaluation sets are colored green or red. The hyper-parameters for model training were picked from (Arjovsky et al., 2019). We perform our analysis over trained models released by Orgad et al. (2022) for randomly picked As discussed in the main text ( 3.1), we primarily see a variation in entropy for later layers in the network.