Low-Budget Label Query through Domain Alignment Enforcement
Jurandy Almeida, Cristiano Saltori, Paolo Rota, and Nicu Sebe

TL;DR
This paper introduces a low-budget label query method that efficiently selects samples for labeling from unlabeled data, leveraging improved domain alignment and prediction consistency to maximize classification accuracy with minimal labeling effort.
Contribution
It presents a novel approach combining enhanced unsupervised domain adaptation with a deterministic sample selection method for low-budget labeling tasks.
Findings
Achieved state-of-the-art results on multiple UDA tasks.
The proposed sample selection method outperforms baselines and competing models.
Effective in maximizing classification accuracy with minimal labeled data.
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…
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
Taxonomy
TopicsDomain Adaptation and Few-Shot Learning · Machine Learning and Data Classification · Multimodal Machine Learning Applications
