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Recent reports suggest that a generic supervised deep CNN model trained on a large-scale dataset reduces, but does not remove, dataset bias on a standard benchmark.
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Detecting change in data streams
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Learning a similarity metric discriminatively, with application to face verification
S. Chopra, R. Hadsell, and Y. LeCun · 2005
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Integrating structured biological data by kernel maximum mean discrepancy
K. M. Borgwardt, A. Gretton, M. J. Rasch, H.-P. Kriegel, B. Schölkopf, and A. J. Smola · 2006
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Analysis of representations for domain adaptation
S. Ben-David, J. Blitzer, K. Crammer, F. Pereira, et al · 2007
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Learning bounds for domain adaptation
J. Blitzer, K. Crammer, A. Kulesza, F. Pereira, and J. Wortman · 2007
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Frustratingly easy domain adaptation
H. Daumé III · 2007
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Adapting SVM classifiers to data with shifted distributions
J. Yang, R. Yan, and A. Hauptmann · 2007
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Domain adaptation: Learning bounds and algorithms
Y. Mansour, M. Mohri, and A. Rostamizadeh · 2009
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Domain adaptation via transfer component analysis
S. J. Pan, I. W. Tsang, J. T. Kwok, and Q. Yang · 2009
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Exploiting weakly-labeled web images to improve object classification: a domain adaptation approach
L. T. Alessandro Bergamo · 2010
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Adapting visual category models to new domains
K. Saenko, B. Kulis, M. Fritz, and T. Darrell · 2010
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Tabula rasa: Model transfer for object category detection
Y. Aytar and A. Zisserman · 2011
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Domain adaptation for object recognition: An unsupervised approach
R. Gopalan, R. Li, and R. Chellappa · 2011
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What you saw is not what you get: Domain adaptation using asymmetric kernel transforms
B. Kulis, K. Saenko, and T. Darrell · 2011
Cited alongside, same era.
Multimodal deep learning
J. Ngiam, A. Khosla, M. Kim, J. Nam, H. Lee, and A. Y. Ng · 2011
Cited alongside, same era.
Unbiased look at dataset bias
A. Torralba and A. Efros · 2011
Cited alongside, same era.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
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DLID: Deep learning for domain adaptation by interpolating between domains
S. Chopra, S. Balakrishnan, and R. Gopalan · 2013
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Unsupervised visual domain adaptation using subspace alignment
B. Fernando, A. Habrard, M. Sebban, and T. Tuytelaars · 2013
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Rich feature hierarchies for accurate object detection and semantic segmentation
R. Girshick, J. Donahue, T. Darrell, and J. Malik · 2013
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Efficient learning of domain-invariant image representations
J. Hoffman, E. Rodner, J. Donahue, K. Saenko, and T. Darrell · 2013
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Overfeat: Integrated recognition, localization and detection using convolutional networks
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ImageNet Large Scale Visual Recognition Challenge 2012
A. Berg, J. Deng, and L. Fei-Fei · 2012
Cited alongside, same era.
Learning with augmented features for heterogeneous domain adaptation
L. Duan, D. Xu, and I. W. Tsang · 2012
Cited alongside, same era.
Geodesic flow kernel for unsupervised domain adaptation
B. Gong, Y. Shi, F. Sha, and K. Grauman · 2012
Cited alongside, same era.
Discovering latent domains for multisource domain adaptation
J. Hoffman, B. Kulis, T. Darrell, and K. Saenko · 2012
Cited alongside, same era.
Undoing the damage of dataset bias
A. Khosla, T. Zhou, T. Malisiewicz, A. Efros, and A. Torralba · 2012
Cited alongside, same era.
P. Sermanet, D. Eigen, X. Zhang, M. Mathieu, R. Fergus, and Y. LeCun · 2013
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Frustratingly easy NBNN domain adaptation
T. Tommasi and B. Caputo · 2013
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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
J. Donahue, Y. Jia, O. Vinyals, J. Hoffman, N. Zhang, E. Tzeng, and T. Darrell · 2014
Closest in time.
Domain adaptive neural networks for object recognition
M. Ghifary, W. B. Kleijn, and M. Zhang · 2014
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LSDA: Large scale detection through adaptation
J. Hoffman, S. Guadarrama, E. Tzeng, R. Hu, J. Donahue, R. Girshick, T. Darrell, and K. Saenko · 2014
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One-shot learning of supervised deep convolutional models
J. Hoffman, E. Tzeng, J. Donahue, , Y. Jia, K. Saenko, and T. Darrell · 2014
Closest in time.