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Exploiting Unlabeled Data in CNNs by Self-supervised Learning to Rank
For many applications the collection of labeled data is expensive laborious.
Exploitation of unlabeled data during training is thus a long pursued objective
of machine learning. Self-supervised learning addresses this by positing an
auxiliary task (different, but related to the supervised task) for which data
is abundantly available. In this paper, we show how ranking can be used as a
proxy task for some regression problems. As another contribution, we propose an
efficient backpropagation technique for Siamese networks which prevents the
redundant computation introduced by the multi-branch network architecture. We
apply our framework to two regression problems: Image Quality Assessment (IQA)
and Crowd Counting. For both we show how to automatically generate ranked image
sets from unlabeled data. Our results show that networks trained to regress to
the ground truth targets for labeled data and to simultaneously learn to rank
unlabeled data obtain significantly better, state-of-the-art results for both
IQA and crowd counting. In addition, we show that measuring network uncertainty
on the self-supervised proxy task is a good measure of informativeness of
unlabeled data. This can be used to drive an algorithm for active learning and
we show that this reduces labeling effort by up to 50%.Comment: Accepted at TPAMI. (Keywords: Learning from rankings, image quality
assessment, crowd counting, active learning). arXiv admin note: text overlap
with arXiv:1803.0309
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