The NNs were trained five times on the identical train-test split with different initializations. The use of the identical train-test split enables comparisons across seeds (otherwise, different samples would be compared with each other, leading to CKAs close to 0 because there is no more dependence between the samples). CKAs with the linear kernel and the RF kernel were calculated for all combinations of models and random seeds. As shown in Figure , the linear kernel can better differentiate between representations of MNIST because the values along the main diagonal are larger and, hence, the difference to other representations is larger.