  Most existing distance metric learning approaches use fully labeled data to
learn the sample similarities in an embedding space. We present a self-training
framework, SLADE, to improve retrieval performance by leveraging additional
unlabeled data. We first train a teacher model on the labeled data and use it
to generate pseudo labels for the unlabeled data. We then train a student model
on both labels and pseudo labels to generate final feature embeddings. We use
self-supervised representation learning to initialize the teacher model. To
better deal with noisy pseudo labels generated by the teacher network, we
design a new feature basis learning component for the student network, which
learns basis functions of feature representations for unlabeled data. The
learned basis vectors better measure the pairwise similarity and are used to
select high-confident samples for training the student network. We evaluate our
method on standard retrieval benchmarks: CUB-200, Cars-196 and In-shop.
Experimental results demonstrate that our approach significantly improves the
performance over the state-of-the-art methods.
