Train Test # Classes CIFAR10 50,000 10,000 10 CIFAR100 50,000 10,000 100 SVHN 73,257 26,032 10 Table 3: CIFAR10, CIFAR100 and SVHN dataset statistics

The mean and standard error as computed across ten trials is shown. In this section, we expand on Section 3 by providing additional details and experimental results on the scalability of baseline methods and Cluster-Margin. Table 3 contains relevant statistics about the CIFAR10, CIFAR100 and SVHN datasets which have been omitted from the main body of the paper. A.1 Baseline Scalability As discussed in Section 3, we improve BADGE's scalability on certain datasets by partitioning the unlabeled pool into subsets, and running BADGE independently on each subset. Specifically, if the size of the unlabeled pool is n, and k is the batch size, we partition the pool uniformly at random into m sets, and run BADGE independently with a target batch size of k/m in each partition.