Meta Co-Training: Two Views are Better than One
This addresses the challenge of scarce labels in computer vision by improving semi-supervised learning performance, though it is incremental as it builds on co-training methods.
The paper tackles the problem of semi-supervised learning in computer vision by proposing Meta Co-Training, which constructs independent views from pre-trained models when natural views are unavailable, resulting in a ~4.7% reduction in error rate on ImageNet-10% and outperforming prior work on fine-grained datasets.
In many critical 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 representative class of semi-supervised algorithms are co-training algorithms. Co-training algorithms leverage two different models which have access to different independent and sufficient representations or "views" of the data to jointly make better predictions. Each of these models creates pseudo-labels on unlabeled points which are used to improve the other model. We show that in the common case where 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. We present Meta Co-Training, a novel semi-supervised learning algorithm, which has two advantages over co-training: (i) learning is more robust when there is large discrepancy between the information content of the different views, and (ii) does not require retraining from scratch on each iteration. Our method achieves new state-of-the-art performance on ImageNet-10% achieving a ~4.7% reduction in error rate over prior work. Our method also outperforms prior semi-supervised work on several other fine-grained image classification datasets.