  We present DistillFlow, a knowledge distillation approach to learning optical
flow. DistillFlow trains multiple teacher models and a student model, where
challenging transformations are applied to the input of the student model to
generate hallucinated occlusions as well as less confident predictions. Then, a
self-supervised learning framework is constructed: confident predictions from
teacher models are served as annotations to guide the student model to learn
optical flow for those less confident predictions. The self-supervised learning
framework enables us to effectively learn optical flow from unlabeled data, not
only for non-occluded pixels, but also for occluded pixels. DistillFlow
achieves state-of-the-art unsupervised learning performance on both KITTI and
Sintel datasets. Our self-supervised pre-trained model also provides an
excellent initialization for supervised fine-tuning, suggesting an alternate
training paradigm in contrast to current supervised learning methods that
highly rely on pre-training on synthetic data. At the time of writing, our
fine-tuned models ranked 1st among all monocular methods on the KITTI 2015
benchmark, and outperform all published methods on the Sintel Final benchmark.
More importantly, we demonstrate the generalization capability of DistillFlow
in three aspects: framework generalization, correspondence generalization and
cross-dataset generalization.
