The inherent dependency of deep learning models to labeled data is a well-known problem
and one of the barriers that slows down the integration of such methods into different fields of
applied sciences and engineering, in which experimental and numerical methods can easily generate
a colossal amount of unlabeled data. This paper proposes an unsupervised domain adaptation
methodology that mimics the peer review process to label new observations in a different domain
from the training set. The approach evaluates the validity of a hypothesis using domain knowledge
acquired from the training set through a similarity analysis, exploring the projected feature space to
examine the class centroid shifts. The methodology is tested on a binary classification problem, where
synthetic images of cubes and cylinders in different orientations are generated. The methodology
improves the accuracy of the object classifier from 60% to around 90% in the case of a domain shift in
physical feature space without human labeling.
Keywords
unsupervised domain adaptation; pseudo-labeling; transfer learning
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
