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We introduce a new dataset of 293,008 high definition (1360 x 1360 pixels) fashion images paired with item descriptions provided by professional stylists.
Bidirectional recurrent neural networks
Schuster, M., Paliwal, K. K., and General, A · 1997
Earlier work this paper cites.
Automated flower classification over a large number of classes
Nilsback, M.-E. and Zisserman, A · 2008
Earlier work this paper cites.
Visualizing data using t-SNE
van der Maaten, L. and Hinton, G · 2008
Earlier work this paper cites.
Apparel classification with style
Bossard, L., Dantone, M., Leistner, C., Wengert, C., Quack, T., and Van Gool, L · 2012
Earlier work this paper cites.
Describing clothing by semantic attributes
Chen, H., Gallagher, A., and Girod, B · 2012
Earlier work this paper cites.
Street-to-shop: Cross-scenario clothing retrieval via parts alignment and auxiliary set
Liu, S., Song, Z., Liu, G., Xu, C., Lu, H., and Yan, S · 2012
Earlier work this paper cites.
Getting the look: clothing recognition and segmentation for automatic product suggestions in everyday photos
Kalantidis, Y., Kennedy, L., and Li, L.-J · 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., and Bengio, Y · 2014
Earlier work this paper cites.
Microsoft coco: Common objects in context
Lin, T.-Y., Maire, M., Belongie, S., Hays, J., Perona, P., Ramanan, D., Dollár, P., and Zitnick, C. L · 2014
Earlier work this paper cites.
Deep domain adaptation for describing people based on fine-grained clothing attributes
Chen, Q., Huang, J., Feris, R., Brown, L. M., Dong, J., and Yan, S · 2015
Earlier work this paper cites.
Where to buy it: Matching street clothing photos in online shops
Kiapour, M. H., Han, X., Lazebnik, S., Berg, A. C., and Berg, T. L · 2015
Earlier work this paper cites.
Deep learning face attributes in the wild
Liu, Z., Luo, P., Wang, X., and Tang, X · 2015
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Neuroaesthetics in fashion: Modeling the perception of fashionability
Simo-Serra, E., Fidler, S., Moreno-Noguer, F., and Urtasun, R · 2015
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Learning visual clothing style with heterogeneous dyadic co-occurrences
Veit, A., Kovacs, B., Bell, S., McAuley, J., Bala, K., and Belongie, S · 2015
Cited alongside, same era.
Learning from massive noisy labeled data for image classification
Xiao, T., Xia, T., Yang, Y., Huang, C., and Wang, X · 2015
Cited alongside, same era.
Photo-realistic single image super-resolution using a generative adversarial network
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al · 2016
Cited alongside, same era.
Clothes co-parsing via joint image segmentation and labeling with application to clothing retrieval
Unsupervised learning of disentangled representations from video
Denton, E. L. et al · 2017
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Viton: An image-based virtual try-on network
Han, X., Wu, Z., Wu, Z., Yu, R., and Davis, L. S · 2017
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Stacked generative adversarial networks
Huang, X., Li, Y., Poursaeed, O., Hopcroft, J., and Belongie, S · 2017
Later among the works it cites.
Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A · 2017
Later among the works it cites.
Progressive growing of gans for improved quality, stability, and variation
Karras, T., Aila, T., Laine, S., and Lehtinen, J · 2017
Later among the works it cites.
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Liang, X., Lin, L., Yang, W., Luo, P., Huang, J., and Yan, S · 2016
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Deepfashion: Powering robust clothes recognition and retrieval with rich annotations
Liu, Z., Luo, P., Qiu, S., Wang, X., and Tang, X · 2016
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Learning deep representations of fine-grained visual descriptions
Reed, S. E., Akata, Z., Schiele, B., and Lee, H · 2016
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Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
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Amortised map inference for image super-resolution
Sønderby, C. K., Caballero, J., Theis, L., Shi, W., and Huszár, F · 2016
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Unsupervised cross-domain image generation
Taigman, Y., Polyak, A., and Wolf, L · 2016
Cited alongside, same era.
Generative adversarial text to image synthesis
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., and Lee, H
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Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., and Polosukhin, I · 2017
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Barratt, S. and Sharma, R · 2018
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Hierarchical adversarially learned inference
Belghazi, M. I., Rajeswar, S., Mastropietro, O., Rostamzadeh, N., Mitrovic, J., and Courville, A · 2018
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Stochastic video generation with a learned prior
Denton, E. and Fergus, R · 2018
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Photographic text-to-image synthesis with a hierarchically-nested adversarial network
Zhang, Z., Xie, Y., and Yang, L · 2018
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