  Learning-based edge detection has hereunto been strongly supervised with
pixel-wise annotations which are tedious to obtain manually. We study the
problem of self-training edge detection, leveraging the untapped wealth of
large-scale unlabeled image datasets. We design a self-supervised framework
with multi-layer regularization and self-teaching. In particular, we impose a
consistency regularization which enforces the outputs from each of the multiple
layers to be consistent for the input image and its perturbed counterpart. We
adopt L0-smoothing as the 'perturbation' to encourage edge prediction lying on
salient boundaries following the cluster assumption in self-supervised
learning. Meanwhile, the network is trained with multi-layer supervision by
pseudo labels which are initialized with Canny edges and then iteratively
refined by the network as the training proceeds. The regularization and
self-teaching together attain a good balance of precision and recall, leading
to a significant performance boost over supervised methods, with lightweight
refinement on the target dataset. Furthermore, our method demonstrates strong
cross-dataset generality. For example, it attains 4.8% improvement for ODS and
5.8% for OIS when tested on the unseen BIPED dataset, compared to the
state-of-the-art methods.
