Domain Transfer Multiple Kernel Learning

By Lixin Duan, Ivor Wai-Hung Tsang, Dong Xu. 2012

534 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Cancer Research

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Citations in the corpus

A Comprehensive Survey on Transfer Learning (2019, background): “[83] proposes a unified framework termed domain transfer multiple kernel learning (DTMKL).”
Deep Hashing Network for Unsupervised Domain Adaptation (2017, methodology): “Reducing domain disparity through nonlinear alignment of data has been possible with Maximum Mean Discrepancy (MMD) - a measure that provides the distribution difference between two datasets in a reproducing-kernel Hilbert space [13].”
Return of Frustratingly Easy Domain Adaptation (2015, methodology): “In Table 1, we compare our method to ﬁve recent published methods: SVMA (Duan, Tsang, and Xu 2012), DAM (Duan et al. 2009), GFK (Gong et al. 2012), SA (Fer-nando et al. 2013), and TCA (Pan et al. 2009) as well as the no adaptation baseline (NA).”
Learning with Augmented Features for Heterogeneous Domain Adaptation (2012, methodology): “used for diﬀerent research ﬁelds such as natural language processing and computer vision (Blitzer et al., 2006; 2007; Daum´e III, 2007; Duan et al., 2010; 2012b;a; Wu & Dietterich, 2004).”
Deep Transfer Learning with Joint Adaptation Networks (2016, background): “Transfer learning (Pan & Yang, 2010) aims to build learning machines that generalize across different domains following different probability distributions (Sugiyama et al., 2008; Pan et al., 2011; Duan et al., 2012; Gong et al., 2013; Zhang et al., 2013).”
Domain Adaptive Faster R-CNN for Object Detection in the Wild (2018, methodology): “Conventional methods include domain transfer multiple kernel learning [10, 11], asymmetric metric learning [33], subspace interpolation [23], geodesic flow kernel [22], subspace alignment [14], covariance matrix alignment [52, 57], etc.”
Exploiting Low-Rank Structure from Latent Domains for Domain Generalization (2014, methodology): “Domain adaptation techniques, which aim to reduce the domain distribution mismatch when the training and testing samples come from different domains, have been successfully used for a broad range of vision applications such as object recognition and video event recognition [23,17,16,10,11,12,6,7,24]”
Heterogeneous Domain Generalization Via Domain Mixup (2020, background): “Domain adaptation (DA) [2, 3, 4, 5] assumes that we can obtain the samples from the testing data (a.”
Recognizing RGB Images by Learning from RGB-D Data (2014, methodology): “Meanwhile, to cope with the considerable variation in feature distributions, new domain adaptation methods were recently developed for different computer vision applications [15, 22, 14, 16, 7, 6, 10, 8, 13, 1].”
Conditional Adversarial Domain Adaptation (2017, background): “Domain adaptation (Pan & Yang, 2010; Quionero-Candela et al., 2009) generalizes a learner across different domains of different distributions (Sugiyama et al., 2008; Pan et al., 2011; Duan et al., 2012; Gong et al., 2013; Zhang et al., 2013).”
Selective Transfer Machine for Personalized Facial Action Unit Detection (2013, methodology): “Unlike previous crossdomain methods [1, 12, 18], STM will not require labels from a test subject.”
Using convolutional features and a sparse autoencoder for land-use scene classification (2016, methodology): “The approach is inspired from the idea of using pre-learned classifiers (trained from auxiliary data) to learn a target classifier (Yang, Yan, and Hauptmann 2007; Schweikert et al. 2008; Duan, Tsang, and Xu 2012; Duan et al. 2012; Duan, Xu, and Tsang 2012).”
Domain Generalization and Adaptation Using Low Rank Exemplar SVMs (2018, background): “DMAIN adaptation techniques, which aim to reduce the domain distribution mismatch when the training and testing samples come from different domains, have been successfully used for a broad range of vision applications such as object recognition and video event recognition [1], [2], [3], [4], [5], [6”
Partial Transfer Learning with Selective Adversarial Networks (2017, background): “Transfer learning [23] bridges different domains or tasks to mitigate the burden of manual labeling for machine learning [22, 6, 34, 32], computer vision [26, 9, 14] and natural language processing [4].”