131 research outputs found
Wasserstein Distance based Deep Adversarial Transfer Learning for Intelligent Fault Diagnosis
The demand of artificial intelligent adoption for condition-based maintenance
strategy is astonishingly increased over the past few years. Intelligent fault
diagnosis is one critical topic of maintenance solution for mechanical systems.
Deep learning models, such as convolutional neural networks (CNNs), have been
successfully applied to fault diagnosis tasks for mechanical systems and
achieved promising results. However, for diverse working conditions in the
industry, deep learning suffers two difficulties: one is that the well-defined
(source domain) and new (target domain) datasets are with different feature
distributions; another one is the fact that insufficient or no labelled data in
target domain significantly reduce the accuracy of fault diagnosis. As a novel
idea, deep transfer learning (DTL) is created to perform learning in the target
domain by leveraging information from the relevant source domain. Inspired by
Wasserstein distance of optimal transport, in this paper, we propose a novel
DTL approach to intelligent fault diagnosis, namely Wasserstein Distance based
Deep Transfer Learning (WD-DTL), to learn domain feature representations
(generated by a CNN based feature extractor) and to minimize the distributions
between the source and target domains through adversarial training. The
effectiveness of the proposed WD-DTL is verified through 3 transfer scenarios
and 16 transfer fault diagnosis experiments of both unsupervised and supervised
(with insufficient labelled data) learning. We also provide a comprehensive
analysis of the network visualization of those transfer tasks
Multi-source adversarial transfer learning for ultrasound image segmentation with limited similarity
Lesion segmentation of ultrasound medical images based on deep learning
techniques is a widely used method for diagnosing diseases. Although there is a
large amount of ultrasound image data in medical centers and other places,
labeled ultrasound datasets are a scarce resource, and it is likely that no
datasets are available for new tissues/organs. Transfer learning provides the
possibility to solve this problem, but there are too many features in natural
images that are not related to the target domain. As a source domain, redundant
features that are not conducive to the task will be extracted. Migration
between ultrasound images can avoid this problem, but there are few types of
public datasets, and it is difficult to find sufficiently similar source
domains. Compared with natural images, ultrasound images have less information,
and there are fewer transferable features between different ultrasound images,
which may cause negative transfer. To this end, a multi-source adversarial
transfer learning network for ultrasound image segmentation is proposed.
Specifically, to address the lack of annotations, the idea of adversarial
transfer learning is used to adaptively extract common features between a
certain pair of source and target domains, which provides the possibility to
utilize unlabeled ultrasound data. To alleviate the lack of knowledge in a
single source domain, multi-source transfer learning is adopted to fuse
knowledge from multiple source domains. In order to ensure the effectiveness of
the fusion and maximize the use of precious data, a multi-source domain
independent strategy is also proposed to improve the estimation of the target
domain data distribution, which further increases the learning ability of the
multi-source adversarial migration learning network in multiple domains.Comment: Submitted to Applied Soft Computing Journa
Multi-source adversarial transfer learning based on similar source domains with local features
Transfer learning leverages knowledge from other domains and has been
successful in many applications. Transfer learning methods rely on the overall
similarity of the source and target domains. However, in some cases, it is
impossible to provide an overall similar source domain, and only some source
domains with similar local features can be provided. Can transfer learning be
achieved? In this regard, we propose a multi-source adversarial transfer
learning method based on local feature similarity to the source domain to
handle transfer scenarios where the source and target domains have only local
similarities. This method extracts transferable local features between a single
source domain and the target domain through a sub-network. Specifically, the
feature extractor of the sub-network is induced by the domain discriminator to
learn transferable knowledge between the source domain and the target domain.
The extracted features are then weighted by an attention module to suppress
non-transferable local features while enhancing transferable local features. In
order to ensure that the data from the target domain in different sub-networks
in the same batch is exactly the same, we designed a multi-source domain
independent strategy to provide the possibility for later local feature fusion
to complete the key features required. In order to verify the effectiveness of
the method, we made the dataset "Local Carvana Image Masking Dataset". Applying
the proposed method to the image segmentation task of the proposed dataset
achieves better transfer performance than other multi-source transfer learning
methods. It is shown that the designed transfer learning method is feasible for
transfer scenarios where the source and target domains have only local
similarities.Comment: Submitted to Information Fusio
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