Visual Domain Adaptation: A survey of recent advances

In pattern recognition and computer vision, one is often faced with scenarios where the training data used to learn a model have different distribution from the data on which the model is applied. Regardless of the cause, any distributional change that occurs after learning a classifier can degrade its performance at test time. Domain adaptation tries to mitigate this degradation. In this article, we provide a survey of domain adaptation methods for visual recognition. We discuss the merits and drawbacks of existing domain adaptation approaches and identify promising avenues for research in this rapidly evolving field.

[1]  J. Heckman.  Sample selection bias as a specification error , 1979 .

[2]  Azriel Rosenfeld,et al.  Computer Vision , 1988, Adv. Comput..

[3]  David J. Field,et al.  Sparse coding with an overcomplete basis set: A strategy employed by V1? , 1997, Vision Research.

[4]  Rich Caruana,et al.  Multitask Learning , 1998, Encyclopedia of Machine Learning and Data Mining.

[5]  H. Shimodaira,et al.  Improving predictive inference under covariate shift by weighting the log-likelihood function , 2000 .

[6]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[7]  JapkowiczNathalie,et al.  The class imbalance problem: A systematic study , 2002 .

[8]  Nathalie Japkowicz,et al.  The class imbalance problem: A systematic study , 2002, Intell. Data Anal..

[9]  K.A. Gallivan,et al.  Efficient algorithms for inferences on Grassmann manifolds , 2004, IEEE Workshop on Statistical Signal Processing, 2003.

[10]  Azriel Rosenfeld,et al.  Face recognition: A literature survey , 2003, CSUR.

[11]  Bianca Zadrozny,et al.  Learning and evaluating classifiers under sample selection bias , 2004, ICML.

[12]  Yi Lin,et al.  Support Vector Machines for Classification in Nonstandard Situations , 2002, Machine Learning.

[13]  Sebastian Thrun,et al.  Text Classification from Labeled and Unlabeled Documents using EM , 2000, Machine Learning.

[14]  Masashi Sugiyama,et al.  Input-dependent estimation of generalization error under covariate shift , 2005 .

[15]  Geoffrey E. Hinton,et al.  Reducing the Dimensionality of Data with Neural Networks , 2006, Science.

[16]  Stephen P. Boyd,et al.  Convex Optimization , 2004, Algorithms and Theory of Computation Handbook.

[17]  Yee Whye Teh,et al.  A Fast Learning Algorithm for Deep Belief Nets , 2006, Neural Computation.

[18]  Bernhard Schölkopf,et al.  Correcting Sample Selection Bias by Unlabeled Data , 2006, NIPS.

[19]  Yong Yu,et al.  Bridged Refinement for Transfer Learning , 2007, PKDD.

[20]  G. Griffin,et al.  Caltech-256 Object Category Dataset , 2007 .

[21]  Rong Yan,et al.  Cross-domain video concept detection using adaptive svms , 2007, ACM Multimedia.

[22]  Hal Daumé,et al.  Frustratingly Easy Domain Adaptation , 2007, ACL.

[23]  Rajat Raina,et al.  Self-taught learning: transfer learning from unlabeled data , 2007, ICML '07.

[24]  ChengXiang Zhai,et al.  Instance Weighting for Domain Adaptation in NLP , 2007, ACL.

[25]  Inderjit S. Dhillon,et al.  Information-theoretic metric learning , 2006, ICML '07.

[26]  Qiang Yang,et al.  Transferring Naive Bayes Classifiers for Text Classification , 2007, AAAI.

[27]  Yoshua. Bengio,et al.  Learning Deep Architectures for AI , 2007, Found. Trends Mach. Learn..

[28]  PROCEssIng magazInE.  IEEE Signal Processing Magazine , 2004 .

[29]  Michael Elad,et al.  Efficient Implementation of the K-SVD Algorithm using Batch Orthogonal Matching Pursuit , 2008 .

[30]  Marwan Mattar,et al.  Labeled Faces in the Wild: A Database forStudying Face Recognition in Unconstrained Environments , 2008 .

[31]  Qiang Yang,et al.  Self-taught clustering , 2008, ICML '08.

[32]  Takeo Kanade,et al.  Multi-PIE , 2008, 2008 8th IEEE International Conference on Automatic Face & Gesture Recognition.

[33]  ChengXiang Zhai,et al.  Domain Adaptation in Natural Language Processing , 2008 .

[34]  Shih-Fu Chang,et al.  Cross-domain learning methods for high-level visual concept classification , 2008, 2008 15th IEEE International Conference on Image Processing.

[35]  Christoph H. Lampert,et al.  Learning to detect unseen object classes by between-class attribute transfer , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[36]  Ivor W. Tsang,et al.  Domain Transfer SVM for video concept detection , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[37]  Rajat Raina,et al.  Self-taught learning , 2009 .

[38]  Arvind Ganesh,et al.  Fast Convex Optimization Algorithms for Exact Recovery of a Corrupted Low-Rank Matrix , 2009 .

[39]  Ivor W. Tsang,et al.  Domain adaptation from multiple sources via auxiliary classifiers , 2009, ICML '09.

[40]  Ali Farhadi,et al.  Describing objects by their attributes , 2009, 2009 IEEE Conference on Computer Vision and Pattern Recognition.

[41]  Shree K. Nayar,et al.  Attribute and simile classifiers for face verification , 2009, 2009 IEEE 12th International Conference on Computer Vision.

[42]  Michael Elad,et al.  Dictionaries for Sparse Representation Modeling , 2010, Proceedings of the IEEE.

[43]  Trevor Darrell,et al.  Factorized Latent Spaces with Structured Sparsity , 2010, NIPS.

[44]  Michael Elad,et al.  Sparse and Redundant Representations - From Theory to Applications in Signal and Image Processing , 2010 .

[45]  Trevor Darrell,et al.  Adapting Visual Category Models to New Domains , 2010, ECCV.

[46]  Lorenzo Bruzzone,et al.  Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[47]  Yi Ma,et al.  The Augmented Lagrange Multiplier Method for Exact Recovery of Corrupted Low-Rank Matrices , 2010, Journal of structural biology.

[48]  Roger Levy,et al.  A new approach to cross-modal multimedia retrieval , 2010, ACM Multimedia.

[49]  Guillermo Sapiro,et al.  Sparse Representation for Computer Vision and Pattern Recognition , 2010, Proceedings of the IEEE.

[50]  Qiang Yang,et al.  A Survey on Transfer Learning , 2010, IEEE Transactions on Knowledge and Data Engineering.

[51]  Ivor W. Tsang,et al.  Visual Event Recognition in Videos by Learning from Web Data , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[52]  Allen Y. Yang,et al.  Fast L1-Minimization Algorithms For Robust Face Recognition , 2010 .

[53]  Pietro Perona,et al.  Visual Recognition with Humans in the Loop , 2010, ECCV.

[54]  Lorenzo Torresani,et al.  Exploiting weakly-labeled Web images to improve object classification: a domain adaptation approach , 2010, NIPS.

[55]  Yang Wang,et al.  A Discriminative Latent Model of Object Classes and Attributes , 2010, ECCV.

[56]  Alexei A. Efros,et al.  Unbiased look at dataset bias , 2011, CVPR 2011.

[57]  Andrew Zisserman,et al.  Tabula rasa: Model transfer for object category detection , 2011, 2011 International Conference on Computer Vision.

[58]  Dieter Fox,et al.  Hierarchical Matching Pursuit for Image Classification: Architecture and Fast Algorithms , 2011, NIPS.

[59]  David Zhang,et al.  Fisher Discrimination Dictionary Learning for sparse representation , 2011, 2011 International Conference on Computer Vision.

[60]  Yoshua Bengio,et al.  Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach , 2011, ICML.

[61]  Trevor Darrell,et al.  Learning cross-modality similarity for multinomial data , 2011, 2011 International Conference on Computer Vision.

[62]  Ethem Alpaydin,et al.  Multiple Kernel Learning Algorithms , 2011, J. Mach. Learn. Res..

[63]  D. Jacobs,et al.  Bypassing synthesis: PLS for face recognition with pose, low-resolution and sketch , 2011, CVPR 2011.

[64]  Rama Chellappa,et al.  Domain adaptation for object recognition: An unsupervised approach , 2011, 2011 International Conference on Computer Vision.

[65]  Trevor Darrell,et al.  What you saw is not what you get: Domain adaptation using asymmetric kernel transforms , 2011, CVPR 2011.

[66]  Ivor W. Tsang,et al.  Domain Adaptation from Multiple Sources : A Domain-Dependent Regularization Approach , 2012 .

[67]  Trevor Darrell,et al.  Discovering Latent Domains for Multisource Domain Adaptation , 2012, ECCV.

[68]  Rama Chellappa,et al.  Sparse Embedding: A Framework for Sparsity Promoting Dimensionality Reduction , 2012, ECCV.

[69]  Rama Chellappa,et al.  Domain Adaptive Dictionary Learning , 2012, ECCV.

[70]  Ivor W. Tsang,et al.  Domain Transfer Multiple Kernel Learning , 2012, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[71]  Dong Xu,et al.  Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[72]  Yuan Shi,et al.  Information-Theoretical Learning of Discriminative Clusters for Unsupervised Domain Adaptation , 2012, ICML.

[73]  David W. Jacobs,et al.  Generalized Multiview Analysis: A discriminative latent space , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[74]  Alexei A. Efros,et al.  Undoing the Damage of Dataset Bias , 2012, ECCV.

[75]  Amit R.Sharma,et al.  Face Photo-Sketch Synthesis and Recognition , 2012 .

[76]  Yuan Shi,et al.  Geodesic flow kernel for unsupervised domain adaptation , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[77]  Boqing Gong,et al.  Overcoming Dataset Bias : An Unsupervised Domain Adaptation Approach , 2012 .

[78]  Kilian Q. Weinberger,et al.  Marginalized Denoising Autoencoders for Domain Adaptation , 2012, ICML.

[79]  Dong Liu,et al.  Robust visual domain adaptation with low-rank reconstruction , 2012, 2012 IEEE Conference on Computer Vision and Pattern Recognition.

[80]  Rama Chellappa,et al.  A Grassmann manifold-based domain adaptation approach , 2012, Proceedings of the 21st International Conference on Pattern Recognition (ICPR2012).

[81]  Pong C. Yuen,et al.  Domain Transfer Support Vector Ranking for Person Re-identification without Target Camera Label Information , 2013, 2013 IEEE International Conference on Computer Vision.

[82]  Tinne Tuytelaars,et al.  Unsupervised Visual Domain Adaptation Using Subspace Alignment , 2013, 2013 IEEE International Conference on Computer Vision.

[83]  Feiping Nie,et al.  Robust and Discriminative Self-Taught Learning , 2013, ICML.

[84]  Hien Van Nguyen,et al.  Non-linear and Sparse Representations for Multi-Modal Recognition , 2013 .

[85]  Trevor Darrell,et al.  Efficient Learning of Domain-invariant Image Representations , 2013, ICLR.

[86]  Wei Wang,et al.  Learning Coupled Feature Spaces for Cross-Modal Matching , 2013, 2013 IEEE International Conference on Computer Vision.

[87]  Vidit Jain,et al.  Adapting Classification Cascades to New Domains , 2013, 2013 IEEE International Conference on Computer Vision.

[88]  Trevor Darrell,et al.  Semi-supervised Domain Adaptation with Instance Constraints , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[89]  Rama Chellappa,et al.  Subspace Interpolation via Dictionary Learning for Unsupervised Domain Adaptation , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[90]  G. Sapiro,et al.  A collaborative framework for 3D alignment and classification of heterogeneous subvolumes in cryo-electron tomography. , 2013, Journal of structural biology.

[91]  Sumit Chopra,et al.  DLID: Deep Learning for Domain Adaptation by Interpolating between Domains , 2013 .

[92]  Barbara Caputo,et al.  Frustratingly Easy NBNN Domain Adaptation , 2013, 2013 IEEE International Conference on Computer Vision.

[93]  Vishal M. Patel,et al.  Joint Hierarchical Domain Adaptation and Feature Learning , 2013 .

[94]  Brian C. Lovell,et al.  Unsupervised Domain Adaptation by Domain Invariant Projection , 2013, 2013 IEEE International Conference on Computer Vision.

[95]  Z. Jane Wang,et al.  Cross-Domain Object Recognition Via Input-Output Kernel Analysis , 2013, IEEE Transactions on Image Processing.

[96]  Yong Yu,et al.  Robust Recovery of Subspace Structures by Low-Rank Representation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[97]  Adriana Kovashka,et al.  Attribute Adaptation for Personalized Image Search , 2013, 2013 IEEE International Conference on Computer Vision.

[98]  Kristen Grauman,et al.  Connecting the Dots with Landmarks: Discriminatively Learning Domain-Invariant Features for Unsupervised Domain Adaptation , 2013, ICML.

[99]  Allen Y. Yang,et al.  Fast L1-Minimization Algorithms For Robust Face Recognition , 2010, 1007.3753.

[100]  Kristen Grauman,et al.  Reshaping Visual Datasets for Domain Adaptation , 2013, NIPS.

[101]  Rama Chellappa,et al.  Generalized Domain-Adaptive Dictionaries , 2013, 2013 IEEE Conference on Computer Vision and Pattern Recognition.

[102]  Rama Chellappa,et al.  Sparse Embedding-based Domain Adaptation for Object Recognition | NIST , 2013 .

[103]  Raghuraman Gopalan,et al.  Model-Driven Domain Adaptation on Product Manifolds for Unconstrained Face Recognition , 2014, International Journal of Computer Vision.

[104]  I. Tsang,et al.  Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation. , 2014, IEEE transactions on pattern analysis and machine intelligence.

[105]  Ivor W. Tsang,et al.  Learning With Augmented Features for Supervised and Semi-Supervised Heterogeneous Domain Adaptation , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[106]  Kristen Grauman,et al.  Learning Kernels for Unsupervised Domain Adaptation with Applications to Visual Object Recognition , 2014, International Journal of Computer Vision.

[107]  Rama Chellappa,et al.  Unsupervised Adaptation Across Domain Shifts by Generating Intermediate Data Representations , 2014, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[108]  Trevor Darrell,et al.  DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition , 2013, ICML.

[109]  Xiaojin Zhu,et al.  Semi-Supervised Learning , 2010, Encyclopedia of Machine Learning.