  Deploying deep models in real-world scenarios entails a number of challenges,
including computational efficiency and real-world (e.g., long-tailed) data
distributions. We address the combined challenge of learning long-tailed
distributions using highly resource-efficient binary neural networks as
backbones. Specifically, we propose a calibrate-and-distill framework that uses
off-the-shelf pretrained full-precision models trained on balanced datasets to
use as teachers for distillation when learning binary networks on long-tailed
datasets. To better generalize to various datasets, we further propose a novel
adversarial balancing among the terms in the objective function and an
efficient multiresolution learning scheme. We conducted the largest empirical
study in the literature using 15 datasets, including newly derived long-tailed
datasets from existing balanced datasets, and show that our proposed method
outperforms prior art by large margins (>14.33% on average).
