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Unsupervised model transfer has the potential to greatly improve the generalizability of deep models to novel domains.
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Caltech-256 object category dataset
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Learning from multiple sources
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Exploiting web images for event recognition in consumer videos: A multiple source domain adaptation approach
Duan, L., Xu, D., and Chang, S.-F · 2012
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Geodesic flow kernel for unsupervised domain adaptation
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Imagenet classification with deep convolutional neural networks
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Representation learning: A review and new perspectives
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Domain adaptive neural networks for object recognition
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Generative adversarial nets
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Continuous manifold based adaptation for evolving visual domains
Hoffman, J., Darrell, T., and Saenko, K · 2014
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Adam: A method for stochastic optimization
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Semi-supervised learning with deep generative models
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Stochastic backpropagation and approximate inference in deep generative models
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Deep transfer learning with joint adaptation networks
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Conditional image synthesis with auxiliary classifier GANs
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Adversarial discriminative domain adaptation
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Dualgan: Unsupervised dual learning for image-to-image translation
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Central moment discrepancy (CMD) for domain-invariant representation learning
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Unpaired image-to-image translation using cycle-consistent adversarial networks
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Unsupervised domain adaptation by backpropagation
Ganin, Y. and Lempitsky, V · 2015
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Batch normalization: Accelerating deep network training by reducing internal covariate shift
Ioffe, S. and Szegedy, C · 2015
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Learning transferable features with deep adaptation networks
Long, M., Cao, Y., Wang, J., and Jordan, M · 2015
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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Coupled generative adversarial networks
Liu, M.-Y. and Tuzel, O · 2016
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Unsupervised domain adaptation with residual transfer networks
Long, M., Zhu, H., Wang, J., and Jordan, M. I · 2016
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Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A · 2017
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Mutual information neural estimation
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Dida: Disentangled synthesis for domain adaptation
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Self-ensembling for visual domain adaptation
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CyCADA: Cycle-consistent adversarial domain adaptation
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Diverse image-to-image translation via disentangled representations
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Synthetic to real adaptation with generative correlation alignment networks
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Moment matching for multi-source domain adaptation
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A domain agnostic normalization layer for unsupervised adversarial domain adaptation
Romijnders, R., Meletis, P., and Dubbelman, G · 2018
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Maximum classifier discrepancy for unsupervised domain adaptation
Saito, K., Watanabe, K., Ushiku, Y., and Harada, T · 2018
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Deep cocktail network: Multi-source unsupervised domain adaptation with category shift
Xu, R., Chen, Z., Zuo, W., Yan, J., and Lin, L · 2018
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Ring loss: Convex feature normalization for face recognition
Zheng, Y., Pal, D. K., and Savvides, M · 2018
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