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The difference between sample distributions of public data sets and specific scenes can be very significant. As a result, the deployment of generic human detectors in real-world scenes most often leads to sub-optimal detection performance. To avoid the labor-intensive task of manual annotations, we propose a semi-supervised approach for training deep convolutional networks on partially labeled data...
Domain adaptation (DA) algorithms utilize a label-rich old dataset (domain) to build a machine learning model (classification, detection etc.) in a label-scarce new dataset with different data distribution. Recent approaches transform cross-domain data into a shared subspace by minimizing the shift between their marginal distributions. In this paper, we propose a novel iterative method to learn a...
In this article wearable sensors based human activity recognition is approached with a case where personal data collected has a high inner activity variety. With this kind of approach, the model adaptivity as well as update becomes more important issues for the activity recognition models. In authors' previous article it was shown that with this kind of data the personal models do not always outperform...
Machine learning models deployed in real world applications, operate in a dynamic environment where the datadistribution can change constantly. These changes, calledconcept drifts, cause the performance of the learned modelto degrade over time. As such it is essential to detect andadapt to changes in the data, for the model to be of any realuse. While, model adaptation requires labeled data (for retraining),...
In many real-world tasks a lot of unlabeled data are collected over time and, although they may be useful to improve the quality of classification models, they are usually ignored. Semi-supervised learning techniques combine unlabeled and labeled data to capture more useful information about a particular task. On the other hand, an incremental learning technique can incorporate new information to...
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