Transfer Sparse Coding for Robust Image Representation

By Mingsheng Long, Guiguang Ding, Jianmin Wang, Jiaguang Sun, Yuchen Guo, Philip S. Yu. 2013

197 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Computer Vision and Pattern Recognition

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Citations in the corpus

Few-Shot Adversarial Domain Adaptation (2017, background): “Others find a shared latent space that both domains can be mapped to before classification [35, 2, 39, 13, 14, 41, 37, 38].”
Unified Deep Supervised Domain Adaptation and Generalization (2017, background): “The second one seeks to find a shared latent space for source and target distributions [40, 2, 44, 21, 22, 47, 43].”
Domain Adaptation for Visual Applications: A Comprehensive Survey (2017, background): “ty estimates between of the source and target data. Both the DIP and SIE, involve non-linear optimizations and are solved with the conjugate gradient algorithm [74]. The Transfer Sparse Coding8 (TSC) [138] learns robust sparse representations for classifying cross-domain data accurately. To bring th”
Domain Adaptive Neural Networks for Object Recognition (2014, methodology): “In recent years, many solutions to this problem have been proposed for computer vision applications (Gong et al., 2012, Gopalan et al., 2011, Long et al., 2013, Saenko et al., 2010) and natural language processing (Daum´e-III, 2009, Pan and Yang, 2010).”
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].”
AutoDIAL: Automatic Domain Alignment Layers (2017, background): “Several approaches have been proposed, both considering hand-crafted features [14, 10, 11, 19, 7] and deep models [20, 30, 8, 22, 9, 18].”
Diversify and Match: A Domain Adaptive Representation Learning Paradigm for Object Detection (2019, methodology): “Traditional methods focus on reducing domain discrepancy through instance re-weighting [21, 41, 14] and shallow feature alignment strategies [16, 32].”
Scatter Component Analysis: A Unified Framework for Domain Adaptation and Domain Generalization (2015, methodology): “We compared the classification performance of the following algorithms: 1) a classifier on raw features (Raw), 2) KPCA, 3) Transfer Component Analysis [45], 4) SSTCA, 5)Geodesic Flow Kernel (GFK) [29], 6) Transfer Sparse Coding [23], 7) SubspaceAlignment [31], 8) TJM [15], 9) unsupervised Scatter Co”
Transfer Joint Matching for Unsupervised Domain Adaptation (2014, background): “Our results reveal substantial effects of joint feature matching and instance reweighting for domain adaptation.”
Image-Image Domain Adaptation with Preserved Self-Similarity and Domain-Dissimilarity for Person Re-identification (2017, methodology): “Other methods seek to find a domain-invariant feature space [34, 31, 10, 30, 42, 11, 2].”
Spectral Bisection Tree Guided Deep Adaptive Exemplar Autoencoder for Unsupervised Domain Adaptation (2016, methodology): “Here we adopt the JDA (Long et al. 2013b) that exploits class-wise MMD criterion as the objective for common feature space learning.”
Domain Adaptation for Image Dehazing (2020, background): “Domain adaptation aims to reduce the discrepancy between different domains [1, 6, 20].”
Deep Transfer Network: Unsupervised Domain Adaptation (2015, methodology): “We follow the same preprocessing procedure in [19].”