17,548 research outputs found
Transferable Positive/Negative Speech Emotion Recognition via Class-wise Adversarial Domain Adaptation
Speech emotion recognition plays an important role in building more
intelligent and human-like agents. Due to the difficulty of collecting speech
emotional data, an increasingly popular solution is leveraging a related and
rich source corpus to help address the target corpus. However, domain shift
between the corpora poses a serious challenge, making domain shift adaptation
difficult to function even on the recognition of positive/negative emotions. In
this work, we propose class-wise adversarial domain adaptation to address this
challenge by reducing the shift for all classes between different corpora.
Experiments on the well-known corpora EMODB and Aibo demonstrate that our
method is effective even when only a very limited number of target labeled
examples are provided.Comment: 5 pages, 3 figures, accepted to ICASSP 201
Shape Consistent 2D Keypoint Estimation under Domain Shift
Recent unsupervised domain adaptation methods based on deep architectures
have shown remarkable performance not only in traditional classification tasks
but also in more complex problems involving structured predictions (e.g.
semantic segmentation, depth estimation). Following this trend, in this paper
we present a novel deep adaptation framework for estimating keypoints under
domain shift}, i.e. when the training (source) and the test (target) images
significantly differ in terms of visual appearance. Our method seamlessly
combines three different components: feature alignment, adversarial training
and self-supervision. Specifically, our deep architecture leverages from
domain-specific distribution alignment layers to perform target adaptation at
the feature level. Furthermore, a novel loss is proposed which combines an
adversarial term for ensuring aligned predictions in the output space and a
geometric consistency term which guarantees coherent predictions between a
target sample and its perturbed version. Our extensive experimental evaluation
conducted on three publicly available benchmarks shows that our approach
outperforms state-of-the-art domain adaptation methods in the 2D keypoint
prediction task
- …