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Dataset bias remains a significant barrier towards solving real world computer vision tasks.
Analysis of representations for domain adaptation
Shai Ben-David, John Blitzer, Koby Crammer, Fernando Pereira, et al · 2007
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Learning bounds for domain adaptation
John Blitzer, Koby Crammer, Alex Kulesza, Fernando Pereira, and Jennifer 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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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
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Unbiased look at dataset bias
A. Torralba and A. Efros · 2011
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ImageNet Large Scale Visual Recognition Challenge 2012
A. Berg, J. Deng, and L. Fei-Fei · 2012
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Emergence of object-selective features in unsupervised feature learning
A. Coates, A. Karpathy, and A. Ng · 2012
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Large scale distributed deep networks
J. Dean, G. Corrado, R. Monga, K. Chen, M. Devin, Q. Le, M. Mao, M. Ranzato, A. Senior, P. Tucker, K. Yang, and A. Ng · 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.
DLID: Deep learning for domain adaptation by interpolating between domains
S. Chopra, S. Balakrishnan, and R. Gopalan · 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 · 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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Undoing the damage of dataset bias
A. Khosla, T. Zhou, T. Malisiewicz, A. Efros, and A. Torralba · 2012
Cited alongside, same era.
ImageNet classification with deep convolutional neural networks
A. Krizhevsky, I. Sutskever, and G. E. Hinton · 2012
Cited alongside, same era.
Erik Rodner, Judy Hoffman, Jeff Donahue, Trevor Darrell, and Kate Saenko · 2013
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Visualizing and Understanding Convolutional Networks
M. Zeiler and R. Fergus · 2013
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