2 research outputs found
Domain Adaptation and Image Classification via Deep Conditional Adaptation Network
Unsupervised domain adaptation aims to generalize the supervised model
trained on a source domain to an unlabeled target domain. Marginal distribution
alignment of feature spaces is widely used to reduce the domain discrepancy
between the source and target domains. However, it assumes that the source and
target domains share the same label distribution, which limits their
application scope. In this paper, we consider a more general application
scenario where the label distributions of the source and target domains are not
the same. In this scenario, marginal distribution alignment-based methods will
be vulnerable to negative transfer. To address this issue, we propose a novel
unsupervised domain adaptation method, Deep Conditional Adaptation Network
(DCAN), based on conditional distribution alignment of feature spaces. To be
specific, we reduce the domain discrepancy by minimizing the Conditional
Maximum Mean Discrepancy between the conditional distributions of deep features
on the source and target domains, and extract the discriminant information from
target domain by maximizing the mutual information between samples and the
prediction labels. In addition, DCAN can be used to address a special scenario,
Partial unsupervised domain adaptation, where the target domain category is a
subset of the source domain category. Experiments on both unsupervised domain
adaptation and Partial unsupervised domain adaptation show that DCAN achieves
superior classification performance over state-of-the-art methods. In
particular, DCAN achieves great improvement in the tasks with large difference
in label distributions (6.1\% on SVHN to MNIST, 5.4\% in UDA tasks on
Office-Home and 4.5\% in Partial UDA tasks on Office-Home)
Information Theory and Its Application in Machine Condition Monitoring
Condition monitoring of machinery is one of the most important aspects of many modern industries. With the rapid advancement of science and technology, machines are becoming increasingly complex. Moreover, an exponential increase of demand is leading an increasing requirement of machine output. As a result, in most modern industries, machines have to work for 24 hours a day. All these factors are leading to the deterioration of machine health in a higher rate than before. Breakdown of the key components of a machine such as bearing, gearbox or rollers can cause a catastrophic effect both in terms of financial and human costs. In this perspective, it is important not only to detect the fault at its earliest point of inception but necessary to design the overall monitoring process, such as fault classification, fault severity assessment and remaining useful life (RUL) prediction for better planning of the maintenance schedule. Information theory is one of the pioneer contributions of modern science that has evolved into various forms and algorithms over time. Due to its ability to address the non-linearity and non-stationarity of machine health deterioration, it has become a popular choice among researchers. Information theory is an effective technique for extracting features of machines under different health conditions. In this context, this book discusses the potential applications, research results and latest developments of information theory-based condition monitoring of machineries