Appendix For Recurrent Bayesian Classifier Chains For Exact Multi-Label Classification

For the experiments described in Section 3.5 of the main paper, all methods which required a Bayesian These residuals are obtained by first training a separate classifier per each class, and then calculating the residual as the error between the predicted and ground truth class. Training Hyperparameters For each method, we used a batch size of 128 and a learning rate of 0.001. Each method was trained until convergence for 200 epochs. To validate that our "non-noisy" class conditioning approach is RBCC, and the class ordering implies that each class is predicted before its parent classes. Results are shown in Figure 1.