2 research outputs found
Few-Shot Adversarial Domain Adaptation
This work provides a framework for addressing the problem of supervised
domain adaptation with deep models. The main idea is to exploit adversarial
learning to learn an embedded subspace that simultaneously maximizes the
confusion between two domains while semantically aligning their embedding. The
supervised setting becomes attractive especially when there are only a few
target data samples that need to be labeled. In this few-shot learning
scenario, alignment and separation of semantic probability distributions is
difficult because of the lack of data. We found that by carefully designing a
training scheme whereby the typical binary adversarial discriminator is
augmented to distinguish between four different classes, it is possible to
effectively address the supervised adaptation problem. In addition, the
approach has a high speed of adaptation, i.e. it requires an extremely low
number of labeled target training samples, even one per category can be
effective. We then extensively compare this approach to the state of the art in
domain adaptation in two experiments: one using datasets for handwritten digit
recognition, and one using datasets for visual object recognition.Comment: Accepted to NIPS 2017. arXiv admin note: text overlap with
arXiv:1709.1019
Improving Unsupervised Domain Adaptation with Variational Information Bottleneck
Domain adaptation aims to leverage the supervision signal of source domain to
obtain an accurate model for target domain, where the labels are not available.
To leverage and adapt the label information from source domain, most existing
methods employ a feature extracting function and match the marginal
distributions of source and target domains in a shared feature space. In this
paper, from the perspective of information theory, we show that representation
matching is actually an insufficient constraint on the feature space for
obtaining a model with good generalization performance in target domain. We
then propose variational bottleneck domain adaptation (VBDA), a new domain
adaptation method which improves feature transferability by explicitly
enforcing the feature extractor to ignore the task-irrelevant factors and focus
on the information that is essential to the task of interest for both source
and target domains. Extensive experimental results demonstrate that VBDA
significantly outperforms state-of-the-art methods across three domain
adaptation benchmark datasets