  In many practical computer vision scenarios unlabeled data is plentiful, but
labels are scarce and difficult to obtain. As a result, semi-supervised
learning which leverages unlabeled data to boost the performance of supervised
classifiers have received significant attention in recent literature. One major
class of semi-supervised algorithms is co-training. In co-training two
different models leverage different independent and sufficient "views" of the
data to jointly make better predictions. During co-training each model creates
pseudo labels on unlabeled points which are used to improve the other model. We
show that in the common case when independent views are not available we can
construct such views inexpensively using pre-trained models. Co-training on the
constructed views yields a performance improvement over any of the individual
views we construct and performance comparable with recent approaches in
semi-supervised learning, but has some undesirable properties. To alleviate the
issues present with co-training we present Meta Co-Training which is an
extension of the successful Meta Pseudo Labels approach to two views. Our
method achieves new state-of-the-art performance on ImageNet-10% with very few
training resources, as well as outperforming prior semi-supervised work on
several other fine-grained image classification datasets.

Computer Vision and Pattern Recognition;Machine Learning