Efficient Long-Tailed Recognition on Binary Networks by Calibrating a Pre-trained Model

The authors propose a calibrate-and-distill framework that uses off-the-shelf pre-trained full-precision models trained on balanced datasets as teachers for distillation when learning binary networks on long-tailed datasets. They further propose an adversarial balancing scheme and an efficient multi-resolution learning approach to generalize the method to various long-tailed data distributions.