The scarcity of labeled data has traditionally been the primary hindrance in building scalable supervised deep learning models that can retain adequate performance in the presence of various heterogeneities in sample distributions. Domain adaptation tries to address this issue by adapting features learned from a smaller set of labeled samples to that of the incoming unlabeled samples. The traditional domain adaptation approaches normally consider only a single source of labeled samples, but in real world use cases, labeled samples can originate from multiple-sources – providing motivation for multi-source domain adaptation (MSDA). Several MSDA approaches have been investigated for wearable sensor-based human activity recognition (HAR) in recent times, but their performance improvement compared to single source counterpart remained marginal. To remedy this performance gap that, we explore multiple avenues to align the conditional distributions in addition to the usual alignment of marginal ones. In our investigation, we extend an existing multi-source domain adaptation approach under semi-supervised settings. We assume the availability of partially labeled target domain data and further explore the pseudo labeling usage with a goal to achieve a performance similar to the former. In our experiments on three publicly available datasets, we find that a limited labeled target domain data and pseudo label data boost the performance over the unsupervised approach by 10-35% and 2-6%, respectively, in various domain adaptation scenarios.
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Unified Deep Supervised Domain Adaptation and Generalization
This work provides a unified framework for addressing
the problem of visual supervised domain adaptation and
generalization with deep models. The main idea is to exploit
the Siamese architecture to learn an embedding subspace
that is discriminative, and where mapped visual domains
are semantically aligned and yet maximally separated. The
supervised setting becomes attractive especially when only
few target data samples need to be labeled. In this scenario,
alignment and separation of semantic probability distributions
is difficult because of the lack of data. We found that by
reverting to point-wise surrogates of distribution distances
and similarities provides an effective solution. In addition,
the approach has a high “speed” of adaptation, which requires
an extremely low number of labeled target training
samples, even one per category can be effective. The approach
is extended to domain generalization. For both applications
the experiments show very promising results.
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- NSF-PAR ID:
- 10053759
- Date Published:
- Journal Name:
- IEEE International Conference on Computer Vision (ICCV)
- Page Range / eLocation ID:
- 5716 to 5726
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Conference Paper:
- https://doi.org/10.1109/ICCV.2017.609
