Misc,
Deep Metric Transfer for Label Propagation with Limited Annotated Data
(Dec 20, 2018)
Abstract
We study object recognition under the constraint that each object class is
only represented by very few observations. In such cases, naive supervised
learning would lead to severe over-fitting in deep neural networks due to
limited training data. We tackle this problem by creating much more training
data through label propagation from the few labeled examples to a vast
collection of unannotated images. Our main insight is that such a label
propagation scheme can be highly effective when the similarity metric used for
propagation is learned and transferred from other related domains with lots of
data. We test our approach on semi-supervised learning, transfer learning and
few-shot recognition, where we learn our similarity metric using various
supervised/unsupervised pretraining methods, and transfer it to unlabeled data
across different data distributions. By taking advantage of unlabeled data in
this way, we achieve significant improvements on all three tasks. Notably, our
approach outperforms current state-of-the-art techniques by an absolute \$20\%\$
for semi-supervised learning on CIFAR10, \$10\%\$ for transfer learning from
ImageNet to CIFAR10, and \$6\%\$ for few-shot recognition on mini-ImageNet, when
labeled examples are limited.
