Low-Budget Label Query through Domain Alignment Enforcement
- URL: http://arxiv.org/abs/2001.00238v2
- Date: Sun, 29 Mar 2020 11:43:05 GMT
- Title: Low-Budget Label Query through Domain Alignment Enforcement
- Authors: Jurandy Almeida, Cristiano Saltori, Paolo Rota, and Nicu Sebe
- Abstract summary: We tackle a new problem named low-budget label query.
We first improve an Unsupervised Domain Adaptation (UDA) method to better align source and target domains.
We then propose a simple yet effective selection method based on uniform sampling of the prediction consistency distribution.
- Score: 48.06803561387064
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Deep learning revolution happened thanks to the availability of a massive
amount of labelled data which have contributed to the development of models
with extraordinary inference capabilities. Despite the public availability of a
large quantity of datasets, to address specific requirements it is often
necessary to generate a new set of labelled data. Quite often, the production
of labels is costly and sometimes it requires specific know-how to be
fulfilled. In this work, we tackle a new problem named low-budget label query
that consists in suggesting to the user a small (low budget) set of samples to
be labelled, from a completely unlabelled dataset, with the final goal of
maximizing the classification accuracy on that dataset. In this work we first
improve an Unsupervised Domain Adaptation (UDA) method to better align source
and target domains using consistency constraints, reaching the state of the art
on a few UDA tasks. Finally, using the previously trained model as reference,
we propose a simple yet effective selection method based on uniform sampling of
the prediction consistency distribution, which is deterministic and steadily
outperforms other baselines as well as competing models on a large variety of
publicly available datasets.
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