  One of the greatest obstacles in the adoption of deep neural networks for new
applications is that training the network typically requires a large number of
manually labeled training samples. We empirically investigate the scenario
where one has access to large amounts of unlabeled data but require labeling
only a single prototypical sample per class in order to train a deep network
(i.e., one-shot semi-supervised learning). Specifically, we investigate the
recent results reported in FixMatch for one-shot semi-supervised learning to
understand the factors that affect and impede high accuracies and reliability
for one-shot semi-supervised learning of Cifar-10. For example, we discover
that one barrier to one-shot semi-supervised learning for high-performance
image classification is the unevenness of class accuracy during the training.
These results point to solutions that might enable more widespread adoption of
one-shot semi-supervised training methods for new applications.
