45,498 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
Domain-Adversarial Training of Neural Networks
We introduce a new representation learning approach for domain adaptation, in
which data at training and test time come from similar but different
distributions. Our approach is directly inspired by the theory on domain
adaptation suggesting that, for effective domain transfer to be achieved,
predictions must be made based on features that cannot discriminate between the
training (source) and test (target) domains. The approach implements this idea
in the context of neural network architectures that are trained on labeled data
from the source domain and unlabeled data from the target domain (no labeled
target-domain data is necessary). As the training progresses, the approach
promotes the emergence of features that are (i) discriminative for the main
learning task on the source domain and (ii) indiscriminate with respect to the
shift between the domains. We show that this adaptation behaviour can be
achieved in almost any feed-forward model by augmenting it with few standard
layers and a new gradient reversal layer. The resulting augmented architecture
can be trained using standard backpropagation and stochastic gradient descent,
and can thus be implemented with little effort using any of the deep learning
packages. We demonstrate the success of our approach for two distinct
classification problems (document sentiment analysis and image classification),
where state-of-the-art domain adaptation performance on standard benchmarks is
achieved. We also validate the approach for descriptor learning task in the
context of person re-identification application.Comment: Published in JMLR: http://jmlr.org/papers/v17/15-239.htm
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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