206 research outputs found
Multi-Adversarial Domain Adaptation
Recent advances in deep domain adaptation reveal that adversarial learning
can be embedded into deep networks to learn transferable features that reduce
distribution discrepancy between the source and target domains. Existing domain
adversarial adaptation methods based on single domain discriminator only align
the source and target data distributions without exploiting the complex
multimode structures. In this paper, we present a multi-adversarial domain
adaptation (MADA) approach, which captures multimode structures to enable
fine-grained alignment of different data distributions based on multiple domain
discriminators. The adaptation can be achieved by stochastic gradient descent
with the gradients computed by back-propagation in linear-time. Empirical
evidence demonstrates that the proposed model outperforms state of the art
methods on standard domain adaptation datasets.Comment: AAAI 2018 Oral. arXiv admin note: substantial text overlap with
arXiv:1705.10667, arXiv:1707.0790
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
- …