11,358 research outputs found
Partial Transfer Learning with Selective Adversarial Networks
Adversarial learning has been successfully embedded into deep networks to
learn transferable features, which reduce distribution discrepancy between the
source and target domains. Existing domain adversarial networks assume fully
shared label space across domains. In the presence of big data, there is strong
motivation of transferring both classification and representation models from
existing big domains to unknown small domains. This paper introduces partial
transfer learning, which relaxes the shared label space assumption to that the
target label space is only a subspace of the source label space. Previous
methods typically match the whole source domain to the target domain, which are
prone to negative transfer for the partial transfer problem. We present
Selective Adversarial Network (SAN), which simultaneously circumvents negative
transfer by selecting out the outlier source classes and promotes positive
transfer by maximally matching the data distributions in the shared label
space. Experiments demonstrate that our models exceed state-of-the-art results
for partial transfer learning tasks on several benchmark datasets
Importance Weighted Adversarial Nets for Partial Domain Adaptation
This paper proposes an importance weighted adversarial nets-based method for
unsupervised domain adaptation, specific for partial domain adaptation where
the target domain has less number of classes compared to the source domain.
Previous domain adaptation methods generally assume the identical label spaces,
such that reducing the distribution divergence leads to feasible knowledge
transfer. However, such an assumption is no longer valid in a more realistic
scenario that requires adaptation from a larger and more diverse source domain
to a smaller target domain with less number of classes. This paper extends the
adversarial nets-based domain adaptation and proposes a novel adversarial
nets-based partial domain adaptation method to identify the source samples that
are potentially from the outlier classes and, at the same time, reduce the
shift of shared classes between domains
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