  We present a novel self-taught framework for unsupervised metric learning,
which alternates between predicting class-equivalence relations between data
through a moving average of an embedding model and learning the model with the
predicted relations as pseudo labels. At the heart of our framework lies an
algorithm that investigates contexts of data on the embedding space to predict
their class-equivalence relations as pseudo labels. The algorithm enables
efficient end-to-end training since it demands no off-the-shelf module for
pseudo labeling. Also, the class-equivalence relations provide rich supervisory
signals for learning an embedding space. On standard benchmarks for metric
learning, it clearly outperforms existing unsupervised learning methods and
sometimes even beats supervised learning models using the same backbone
network. It is also applied to semi-supervised metric learning as a way of
exploiting additional unlabeled data, and achieves the state of the art by
boosting performance of supervised learning substantially.
