Model and Feature Diversity for Bayesian Neural Networks in Mutual Learning Supplementary Material

We also test the direct maximization of Kullback-Leibler (KL) divergence between feature distributions. As presented in Table A.1, the direct maximization of Direct maximize KL divergence between feature distributions. We further conduct ablation studies focusing on directly maximizing the Kullback-Leibler (KL) divergence between feature distributions of peer Bayesian neural networks (as in setting d in Table A.1). Table A.2, the results for both ResNet20 and ResNet32 BNN models demonstrate that using optimal "*" means Bayesian neural networks that are initialized with the mean value from the pre-trained The results are shown in Table A.3. Figure A.1: Comparison of optimal transport distance between the parameter distributions of peer A.1, it is clear that our proposed method, which promotes A.2, it is clear that our proposed method, which promotes diversity in the feature