4 research outputs found
Multi-Domain Active Learning: A Comparative Study
Building classifiers on multiple domains is a practical problem in the real
life. Instead of building classifiers one by one, multi-domain learning (MDL)
simultaneously builds classifiers on multiple domains. MDL utilizes the
information shared among the domains to improve the performance. As a
supervised learning problem, the labeling effort is still high in MDL problems.
Usually, this high labeling cost issue could be relieved by using active
learning. Thus, it is natural to utilize active learning to reduce the labeling
effort in MDL, and we refer this setting as multi-domain active learning
(MDAL). However, there are only few works which are built on this setting. And
when the researches have to face this problem, there is no off-the-shelf
solutions. Under this circumstance, combining the current multi-domain learning
models and single-domain active learning strategies might be a preliminary
solution for MDAL problem. To find out the potential of this preliminary
solution, a comparative study over 5 models and 4 selection strategies is made
in this paper. To the best of our knowledge, this is the first work provides
the formal definition of MDAL. Besides, this is the first comparative work for
MDAL problem. From the results, the Multinomial Adversarial Networks (MAN)
model with a simple best vs second best (BvSB) uncertainty strategy shows its
superiority in most cases. We take this combination as our off-the-shelf
recommendation for the MDAL problem