  Recent advances in one-shot semi-supervised learning have lowered the barrier
for deep learning of new applications. However, the state-of-the-art for
semi-supervised learning is slow to train and the performance is sensitive to
the choices of the labeled data and hyper-parameter values. In this paper, we
present a one-shot semi-supervised learning method that trains up to an order
of magnitude faster and is more robust than state-of-the-art methods.
Specifically, we show that by combining semi-supervised learning with a
one-stage, single network version of self-training, our FROST methodology
trains faster and is more robust to choices for the labeled samples and changes
in hyper-parameters. Our experiments demonstrate FROST's capability to perform
well when the composition of the unlabeled data is unknown; that is when the
unlabeled data contain unequal numbers of each class and can contain
out-of-distribution examples that don't belong to any of the training classes.
High performance, speed of training, and insensitivity to hyper-parameters make
FROST the most practical method for one-shot semi-supervised training. Our code
is available at https://github.com/HelenaELiu/FROST.
