13 research outputs found
Select, Label, and Mix: Learning Discriminative Invariant Feature Representations for Partial Domain Adaptation
Partial domain adaptation which assumes that the unknown target label space
is a subset of the source label space has attracted much attention in computer
vision. Despite recent progress, existing methods often suffer from three key
problems: negative transfer, lack of discriminability and domain invariance in
the latent space. To alleviate the above issues, we develop a novel 'Select,
Label, and Mix' (SLM) framework that aims to learn discriminative invariant
feature representations for partial domain adaptation. First, we present a
simple yet efficient "select" module that automatically filters out the outlier
source samples to avoid negative transfer while aligning distributions across
both domains. Second, the "label" module iteratively trains the classifier
using both the labeled source domain data and the generated pseudo-labels for
the target domain to enhance the discriminability of the latent space. Finally,
the "mix" module utilizes domain mixup regularization jointly with the other
two modules to explore more intrinsic structures across domains leading to a
domain-invariant latent space for partial domain adaptation. Extensive
experiments on several benchmark datasets demonstrate the superiority of our
proposed framework over state-of-the-art methods
Clustering-guided novel unsupervised domain adversarial network for partial transfer fault diagnosis of rotating machinery
Unsupervised partial transfer fault diagnosis studies of rotating machinery have practical significance, which still exists some challenges, for example, the learned domain-specific statistics and parameters usually influence the learning effect of target-domain features to some degree, and the relatively scattered target-domain features will lead to negative transfer. To overcome those limitations and further improve partial transfer fault diagnosis performance, a clustering-guided novel unsupervised domain adversarial network is proposed in this paper. Firstly, a novel unsupervised domain adversarial network is constructed using domain-specific batch normalization to remove domain-specific information to enhance alignment between source and target domains. Secondly, embedded clustering strategy is designed to learn tightly clustered target-domain features to suppress negative transfer in partial domain adaptation process. Finally, a joint optimization objective function is defined to balance different losses to improve the training and diagnosis performance. Two experimental cases of bevel gearbox and bearing are used to validate the effectiveness and superiority of the proposed method in solving unsupervised partial transfer fault diagnosis problems