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In this paper, we propose a novel unsupervised domain adaptation algorithm based on deep learning for visual object recognition.
Maximum likelihood estimation of misspecified models
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Domain Adaptation Problems: A DASVM Classification Technique and a Circular Validation Strategy
Bruzzone, L., Marconcini, M.: · 2010
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Adapting Visual Category Models to New Domains
Saenko, K., Kulis, B., Fritz, M., Darrell, T.: · 2010
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Pan, S.J., Yang, Q.: · 2010
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Domain adaptation via transfer component analysis
Pan, S.J., Tsang, I.W.H., Kwok, J.T., Yang, Q.: · 2011
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Unsupervised Visual Domain Adaptation Using Subspace Alignment
Fernando, B., Habrard, A., Sebban, M., Tuytelaars, T.: · 2013
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One-Shot Adaptation of Supervised Deep Convolutional Models
Hoffman, J., Tzeng, E., Donahue, J., Jia, Y., Saenko, K., Darrell, T.: · 2013
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DLID: Deep Learning for Domain Adaptation by Interpolating between Domains
Chopra, S., Balakrishnan, S., Gopalan, R.: · 2013
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Generalized denoising auto-encoders as generative models
Bengio, Y., Yao, L., Guillaume, A., Vincent, P.: · 2013
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Transfer Joint Matching for Unsupervised Domain Adaptation
Long, M., Wang, J., Ding, G., Sun, J., Yu, P.S.: · 2014
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Domain adaptive neural networks for object recognition
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A literature review of domain adaptation with unlabeled data
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Gopalan, R., Li, R., Chellapa, R.: · 2011
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Domain Adaptation for Large-Scale Sentiment Classification: A Deep Learning Approach
Glorot, X., Bordes, A., Bengio, Y.: · 2011
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Stacked Convolutional Auto-Encoders for Hierarchical Feature Extraction
Masci, J., Meier, U., Ciresan, D., Schmidhuber, J.e.: · 2011
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Reading Digits in Natural Images with Unsupervised Feature Learning
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An Analysis of Single-Layer Networks in Unsupervised Feature Learning
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Rich feature hierarchies for accurate object detection and semantic segmentation
Girshick, R., Donahue, J., Darrell, T., Malik, J.: · 2014
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DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition
Donahue, J., Jia, Y., Vinyals, O., Hoffman, J., Zhang, N., Tzeng, E., Darrell, T.: · 2014
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Deep domain confusion: Maximizing for domain invariance
Tzeng, E., Hoffman, J., Zhang, N., Saenko, K., Darrell, T.: · 2014
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Dropout: A Simple Way to Prevent Neural Networks from Overfitting
Srivastava, N., Hinton, G., Krizhevsky, A., Sutskever, I., Salakhutdinov, R.: · 2014
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Face recognition on drones: Issues and limitations
Hsu, H.J., Chen, K.T.: · 2015
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Visual domain adaptation: A survey of recent advances
Patel, V.M., Gopalan, R., Li, R., Chellapa, R.: · 2015
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Landmarks-Based Kernelized Subspace Alignment for Unsupervised Domain Adaptation
Aljundi, R., Emonet, R., Muselet, D., Sebban, M.: · 2015
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Very deep convolutional networks for large-scale image recognition
Simonyan, K., Zisserman, A.: · 2015
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Unsupervised domain adaptation by backpropagation
Ganin, Y., Lempitsky, V.S.: · 2015
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Scatter component analysis: A unified framework for domain adaptation and domain generalization
Ghifary, M., Balduzzi, D., Kleijn, W.B., Zhang, M.: · 2015
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Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., Jordan, M.I.: · 2015
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Simultaneous deep transfer across domains and tasks
Tzeng, E., Hoffman, J., Darrell, T., Saenko, K.: · 2015
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Geodesic Flow Kernel for Unsupervised Domain Adaptation
Gong, B., Shi, Y., Sha, F., Grauman, K.: · 2073
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