10,412 research outputs found
Recent Advances in Transfer Learning for Cross-Dataset Visual Recognition: A Problem-Oriented Perspective
This paper takes a problem-oriented perspective and presents a comprehensive
review of transfer learning methods, both shallow and deep, for cross-dataset
visual recognition. Specifically, it categorises the cross-dataset recognition
into seventeen problems based on a set of carefully chosen data and label
attributes. Such a problem-oriented taxonomy has allowed us to examine how
different transfer learning approaches tackle each problem and how well each
problem has been researched to date. The comprehensive problem-oriented review
of the advances in transfer learning with respect to the problem has not only
revealed the challenges in transfer learning for visual recognition, but also
the problems (e.g. eight of the seventeen problems) that have been scarcely
studied. This survey not only presents an up-to-date technical review for
researchers, but also a systematic approach and a reference for a machine
learning practitioner to categorise a real problem and to look up for a
possible solution accordingly
MiniMax Entropy Network: Learning Category-Invariant Features for Domain Adaptation
How to effectively learn from unlabeled data from the target domain is
crucial for domain adaptation, as it helps reduce the large performance gap due
to domain shift or distribution change. In this paper, we propose an
easy-to-implement method dubbed MiniMax Entropy Networks (MMEN) based on
adversarial learning. Unlike most existing approaches which employ a generator
to deal with domain difference, MMEN focuses on learning the categorical
information from unlabeled target samples with the help of labeled source
samples. Specifically, we set an unfair multi-class classifier named
categorical discriminator, which classifies source samples accurately but be
confused about the categories of target samples. The generator learns a common
subspace that aligns the unlabeled samples based on the target pseudo-labels.
For MMEN, we also provide theoretical explanations to show that the learning of
feature alignment reduces domain mismatch at the category level. Experimental
results on various benchmark datasets demonstrate the effectiveness of our
method over existing state-of-the-art baselines.Comment: 8 pages, 6 figure
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