862 research outputs found
Mining Label Distribution Drift in Unsupervised Domain Adaptation
Unsupervised domain adaptation targets to transfer task knowledge from
labeled source domain to related yet unlabeled target domain, and is catching
extensive interests from academic and industrial areas. Although tremendous
efforts along this direction have been made to minimize the domain divergence,
unfortunately, most of existing methods only manage part of the picture by
aligning feature representations from different domains. Beyond the discrepancy
in feature space, the gap between known source label and unknown target label
distribution, recognized as label distribution drift, is another crucial factor
raising domain divergence, and has not been paid enough attention and well
explored. From this point, in this paper, we first experimentally reveal how
label distribution drift brings negative effects on current domain adaptation
methods. Next, we propose Label distribution Matching Domain Adversarial
Network (LMDAN) to handle data distribution shift and label distribution drift
jointly. In LMDAN, label distribution drift problem is addressed by the
proposed source samples weighting strategy, which select samples to contribute
to positive adaptation and avoid negative effects brought by the mismatched in
label distribution. Finally, different from general domain adaptation
experiments, we modify domain adaptation datasets to create the considerable
label distribution drift between source and target domain. Numerical results
and empirical model analysis show that LMDAN delivers superior performance
compared to other state-of-the-art domain adaptation methods under such
scenarios
ContrasGAN : unsupervised domain adaptation in Human Activity Recognition via adversarial and contrastive learning
Human Activity Recognition (HAR) makes it possible to drive applications directly from embedded and wearable sensors. Machine learning, and especially deep learning, has made significant progress in learning sensor features from raw sensing signals with high recognition accuracy. However, most techniques need to be trained on a large labelled dataset, which is often difficult to acquire. In this paper, we present ContrasGAN, an unsupervised domain adaptation technique that addresses this labelling challenge by transferring an activity model from one labelled domain to other unlabelled domains. ContrasGAN uses bi-directional generative adversarial networks for heterogeneous feature transfer and contrastive learning to capture distinctive features between classes. We evaluate ContrasGAN on three commonly-used HAR datasets under conditions of cross-body, cross-user, and cross-sensor transfer learning. Experimental results show a superior performance of ContrasGAN on all these tasks over a number of state-of-the-art techniques, with relatively low computational cost.PostprintPeer reviewe
centroIDA: Cross-Domain Class Discrepancy Minimization Based on Accumulative Class-Centroids for Imbalanced Domain Adaptation
Unsupervised Domain Adaptation (UDA) approaches address the covariate shift
problem by minimizing the distribution discrepancy between the source and
target domains, assuming that the label distribution is invariant across
domains. However, in the imbalanced domain adaptation (IDA) scenario, covariate
and long-tailed label shifts both exist across domains. To tackle the IDA
problem, some current research focus on minimizing the distribution
discrepancies of each corresponding class between source and target domains.
Such methods rely much on the reliable pseudo labels' selection and the feature
distributions estimation for target domain, and the minority classes with
limited numbers makes the estimations more uncertainty, which influences the
model's performance. In this paper, we propose a cross-domain class discrepancy
minimization method based on accumulative class-centroids for IDA (centroIDA).
Firstly, class-based re-sampling strategy is used to obtain an unbiased
classifier on source domain. Secondly, the accumulative class-centroids
alignment loss is proposed for iterative class-centroids alignment across
domains. Finally, class-wise feature alignment loss is used to optimize the
feature representation for a robust classification boundary. A series of
experiments have proved that our method outperforms other SOTA methods on IDA
problem, especially with the increasing degree of label shift
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