335 research outputs found
Unsupervised Domain Adaptation using Graph Transduction Games
Unsupervised domain adaptation (UDA) amounts to assigning class labels to the
unlabeled instances of a dataset from a target domain, using labeled instances
of a dataset from a related source domain. In this paper, we propose to cast
this problem in a game-theoretic setting as a non-cooperative game and
introduce a fully automatized iterative algorithm for UDA based on graph
transduction games (GTG). The main advantages of this approach are its
principled foundation, guaranteed termination of the iterative algorithms to a
Nash equilibrium (which corresponds to a consistent labeling condition) and
soft labels quantifying the uncertainty of the label assignment process. We
also investigate the beneficial effect of using pseudo-labels from linear
classifiers to initialize the iterative process. The performance of the
resulting methods is assessed on publicly available object recognition
benchmark datasets involving both shallow and deep features. Results of
experiments demonstrate the suitability of the proposed game-theoretic approach
for solving UDA tasks.Comment: Oral IJCNN 201
Transductive Learning with String Kernels for Cross-Domain Text Classification
For many text classification tasks, there is a major problem posed by the
lack of labeled data in a target domain. Although classifiers for a target
domain can be trained on labeled text data from a related source domain, the
accuracy of such classifiers is usually lower in the cross-domain setting.
Recently, string kernels have obtained state-of-the-art results in various text
classification tasks such as native language identification or automatic essay
scoring. Moreover, classifiers based on string kernels have been found to be
robust to the distribution gap between different domains. In this paper, we
formally describe an algorithm composed of two simple yet effective
transductive learning approaches to further improve the results of string
kernels in cross-domain settings. By adapting string kernels to the test set
without using the ground-truth test labels, we report significantly better
accuracy rates in cross-domain English polarity classification.Comment: Accepted at ICONIP 2018. arXiv admin note: substantial text overlap
with arXiv:1808.0840
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
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