Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy

By Lorenzo Bruzzone, Mattia Marconcini. 2010

609 citations recorded by the source corpus.

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In Artificial Intelligence

Citations in the corpus

Domain-Adversarial Training of Neural Networks (2015, background): “Over the years, a large part of the literature has focused mainly on linear hypothesis (see for instance Blitzer et al., 2006; Bruzzone and Marconcini, 2010; Germain et al., 2013; Baktashmotlagh et al., 2013; Cortes and Mohri, 2014).”
Deep Hashing Network for Unsupervised Domain Adaptation (2017, methodology): “One straightforward approach is, to modify a classifier trained for the source data by adapting it to classify target data [1, 4] or learn a transformation matrix to linearly transform the source data, so that it is aligned with the target [27, 42].”
Domain-Adversarial Neural Networks (2014, background): “A large part of the literature however has focused mainly on linear hypothesis (see for instance Blitzer et al., 2006; Bruzzone & Marconcini, 2010; Germain et al., 2013; Baktashmot-lagh et al., 2013; Cortes & Mohri, 2014).”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, methodology): “The domain adaptation SVM (DASVM) [23] exploits within the semi-supervised DA scenario both the transductive SVM [123] and its extension, the progressive transductive SVM [38].”
WILDS: A Benchmark of in-the-Wild Distribution Shifts (2020, neutral): “…(Saenko et al., 2010; Gong et al., 2012), sentiment analysis (Blitzer et al., 2007; Pan et al., 2010), part-of-speech tagging (Marcus et al., 1993; Daumé III, 2007), flow cytometry (Blanchard et al., 2011; Muandet et al., 2013), or land cover classification (Bruzzone and Marconcini, 2009).”
Contrastive Adaptation Network for Unsupervised Domain Adaptation (2019, methodology): “In the absence of labeled data from the target domain, Unsupervised Domain Adaptation (UDA) methods have emerged to mitigate the domain shift in data distributions [2, 1, 5, 37, 30, 18, 3, 17].”
A Deeper Look at Dataset Bias (2015, methodology): “This technique is known as self-labelling [3,20] and starts by annotating the target with a classifier trained on the source.”
Deep Reconstruction-Classification Networks for Unsupervised Domain Adaptation (2016, background): “Another issue is that many existing algorithms require optimization procedures that do not scale well as the size of datasets increases [4,5,6,7,8,9,10].”
Domain Transfer Multiple Kernel Learning (2012, background): “Very recently, Bruzzone and Marconcini [6] proposed Domain Adaptation Support Vector Machine (DASVM), which extended Transductive SVM (T-SVM) to label unlabeled target patterns progressively and simultaneously remove some auxiliary labeled patterns.”
Small Sample Learning in Big Data Era (2018, methodology): “Along this research direction, cross-domain SVM (Jiang et al., 2008), domain transfer SVM (Duan et al., 2009), domain adaptation SVM (Bruzzone and Marconcini, 2010), adaptive multiple kernel learning (A-MKL) (Duan et al., 2012), and residual transfer network (RTN) (Long et al., 2016) have been progr”
Frustratingly Easy NBNN Domain Adaptation (2013, methodology): “They are mainly based on max-margin methods associated with strategies to adapt the learning parameters to novel problems [10, 29, 7].”
Domain Adaptation for the Classification of Remote Sensing Data: An Overview of Recent Advances (2016, methodology): “To address this crucial issue, a circular validation strategy is presented and applied to remote sensing images in [64].”
Domain Adaptation on the Statistical Manifold (2014, background): “Unsupervised domain adaptation techniques therefore emerged as a solution to this challenging scenario where no labeled data is available from the target domain [34, 5, 8, 27].”
Transfer Joint Matching for Unsupervised Domain Adaptation (2014, background): “However, in visual domain adaptation, the domain difference is substantially large and it is difﬁcult to deﬁne the relevance of raw features to different domains.”