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Given a single image x from domain A and a set of images from domain B, our task is to generate the analogous of x in B.
Earth mover’s distance minimization for unsupervised bilingual lexicon induction
Zhang, M., Liu, Y., Luan, H., Sun, M.: · 1945
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Adversarial training for unsupervised bilingual lexicon induction
Zhang, M., Liu, Y., Luan, H., Sun, M.: · 1970
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An IR approach for translating new words from nonparallel, comparable texts
Fung, P., Yee, L.Y.: · 1998
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Automatic identification of word translations from unrelated english and german corpora
Rapp, R.: · 1999
Earlier work this paper cites.
Inducing translation lexicons via diverse similarity measures and bridge languages
Schafer, C., Yarowsky, D.: · 2002
Earlier work this paper cites.
Learning a translation lexicon from monolingual corpora
Koehn, P., Knight, K.: · 2002
Earlier work this paper cites.
The Way We Think: Conceptual Blending and the Mind’s Hidden Complexities
Fauconnier, G., Turner, M.: · 2003
Earlier work this paper cites.
MNIST handwritten digit database
LeCun, Y., Cortes, C.: · 2010
Earlier work this paper cites.
Reading digits in natural images with unsupervised feature learning
Netzer, Y., Wang, T., Coates, A., Bissacco, A., Wu, B., Ng, A.Y.: · 2011
Earlier work this paper cites.
Efficient estimation of word representations in vector space
Mikolov, T., Chen, K., Corrado, G., Dean, J.: · 2013
Earlier work this paper cites.
Spatial pattern templates for recognition of objects with regular structure
Tyleček, R., Šára, R.: · 2013
Earlier work this paper cites.
Generative adversarial nets
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: · 2014
Cited alongside, same era.
How transferable are features in deep neural networks?
Yosinski, J., Clune, J., Bengio, Y., Lipson, H.: · 2014
Cited alongside, same era.
Unsupervised representation learning with deep convolutional generative adversarial networks
Radford, A., Metz, L., Chintala, S.: · 2015
Cited alongside, same era.
Coupled generative adversarial networks
Liu, M.Y., Tuzel, O.: · 2016
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Multi-class generative adversarial networks with the l2 loss function
Mao, X., Li, Q., Xie, H., Lau, R., Wang, Z.: · 2016
Cited alongside, same era.
Perceptual losses for real-time style transfer and super-resolution
Johnson, J., Alahi, A., Fei-Fei, L.: · 2016
One-sided unsupervised domain mapping
Benaim, S., Wolf, L.: · 2017
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Unsupervised image-to-image translation networks
Liu, M.Y., Breuel, T., Kautz, J.: · 2017
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Word translation without parallel data
Conneau, A., Lample, G., Ranzato, M., Denoyer, L., Jégou, H.: · 2017
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Unsupervised cross-domain image generation
Taigman, Y., Polyak, A., Wolf, L.: · 2017
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Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: · 2017
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Stargan: Unified generative adversarial networks for multi-domain image-to-image translation
Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: · 2018
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Cited alongside, same era.
The cityscapes dataset for semantic urban scene understanding
Cordts, M., Omran, M., Ramos, S., Rehfeld, T., Enzweiler, M., Benenson, R., Franke, U., Roth, S., Schiele, B.: · 2016
Cited alongside, same era.
Unpaired image-to-image translation using cycle-consistent adversarial networks
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: · 2017
Cited alongside, same era.
Learning to discover cross-domain relations with generative adversarial networks
Kim, T., Cha, M., Kim, H., Lee, J., Kim, J.: · 2017
Cited alongside, same era.
Dualgan: Unsupervised dual learning for image-to-image translation
Yi, Z., Zhang, H., Tan, P., Gong, M.: · 2017
Cited alongside, same era.
Unsupervised machine translation using monolingual corpora only
Lample, G., Conneau, A., Denoyer, L., Ranzato, M.: · 2018
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Non-adversarial unsupervised word translation
Hoshen, Y., Wolf, L.: · 2018
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The role of minimal complexity functions in unsupervised learning of semantic mappings
Galanti, T., Wolf, L., Benaim, S.: · 2018
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NAM - unsupervised cross-domain image mapping without cycles or GANs
Hoshen, Y., Wolf, L.: · 2018
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