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Learning inter-domain mappings from unpaired data can improve performance in structured prediction tasks, such as image segmentation, by reducing the need for paired data.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Fine-grained visual comparisons with local learning
Yu, A. and Grauman, K · 2014
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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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Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., and Chintala, S · 2015
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Infogan: Interpretable representation learning by information maximizing generative adversarial nets
Chen, X., Duan, Y., Houthooft, R., Schulman, J., Sutskever, I., and Abbeel, P · 2016
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Pixelvae: A latent variable model for natural images
Gulrajani, I., Kumar, K., Ahmed, F., Taiga, A. A., Visin, F., Vazquez, D., and Courville, A · 2016
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Coupled generative adversarial networks
Liu, M.-Y. and Tuzel, O · 2016
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Adversarial autoencoders
Makhzani, A., Shlens, J., Jaitly, N., and Goodfellow, I · 2016
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Unrolled generative adversarial networks
Metz, L., Poole, B., Pfau, D., and Sohl-Dickstein, J · 2016
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Generative visual manipulation on the natural image manifold
Zhu, J.-Y., Krähenbühl, P., Shechtman, E., and Efros, A. A · 2016
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Cyclegan: a master of steganography
Chu, C., Zhmoginov, A., and Sandler, M · 2017
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Triangle generative adversarial networks
Gan, Z., Chen, L., Wang, W., Pu, Y., Zhang, Y., Liu, H., Li, C., and Carin, L · 2017
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Cycada: Cycle-consistent adversarial domain adaptation
Hoffman, J., Tzeng, E., Park, T., Zhu, J.-Y., Isola, P., Saenko, K., Efros, A. A., and Darrell, T · 2017
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Unsupervised image-to-image translation networks
Liu, M.-Y., Breuel, T., and Kautz, J · 2017
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Film: Visual reasoning with a general conditioning layer
Perez, E., Strub, F., De Vries, H., Dumoulin, V., and Courville, A · 2017
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Xgan: Unsupervised image-to-image translation for many-to-many mappings
Royer, A., Bousmalis, K., Gouws, S., Bertsch, F., Moressi, I., Cole, F., and Murphy, K · 2017
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Style transfer from non-parallel text by cross-alignment
Shen, T., Lei, T., Barzilay, R., and Jaakkola, T · 2017
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Adversarial inverse graphics networks: Learning 2d-to-3d lifting and image-to-image translation from unpaired supervision
Tung, H.-Y. F., Harley, A. W., Seto, W., and Fragkiadaki, K · 2017
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Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A · 2017
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Learning to discover cross-domain relations with generative adversarial networks
Kim, T., Cha, M., Kim, H., Lee, J., and Kim, J · 2017
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Unsupervised machine translation using monolingual corpora only
Lample, G., Denoyer, L., and Ranzato, M · 2017
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Alice: Towards understanding adversarial learning for joint distribution matching
Li, C., Liu, H., Chen, C., Pu, Y., Chen, L., Henao, R., and Carin, L · 2017
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A learned representation for artistic style
Dumoulin, V., Shlens, J., and Kudlur, M
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Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.-Y., Park, T., Isola, P., and Efros, A. A
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Toward multimodal image-to-image translation
Zhu, J.-Y., Zhang, R., Pathak, D., Darrell, T., Efros, A. A., Wang, O., and Shechtman, E
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Unsupervised creation of parameterized avatars
Wolf, L., Taigman, Y., and Polyak, A · 2017
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On the effects of batch and weight normalization in generative adversarial networks
Xiang, S. and Li, H · 2017
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Dualgan: Unsupervised dual learning for image-to-image translation
Yi, Z., Zhang, H., Gong, P. T., et al · 2017
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The unreasonable effectiveness of deep networks as a perceptual metric
Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O · 2018
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