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Text-to-image generation has traditionally focused on finding better modeling assumptions for training on a fixed dataset.
Tensor product variable binding and the representation of symbolic structures in connectionist systems
Smolensky, P · 1990
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Gradient-based learning applied to document recognition
LeCun, Y., Bottou, L., Bengio, Y., and Haffner, P · 1998
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Dm-gan: Dynamic memory generative adversarial networks for text-to-image synthesis
Zhu, M., Pan, P., Chen, W., and Yang, Y · 1998
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Imagenet: A large-scale hierarchical image database
Deng, J., Dong, W., Socher, R., Li, L.-J., Li, K., and Fei-Fei, L · 2009
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Caltech-ucsd birds 200
Welinder, P., Branson, S., Mita, T., Wah, C., Schroff, F., Belongie, S., and Perona, P · 2010
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Estimating or propagating gradients through stochastic neurons for conditional computation
Bengio, Y., Léonard, N., and Courville, A · 2013
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Auto-encoding variational bayes
Kingma, D. P. and Welling, M · 2013
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Generative adversarial networks
Goodfellow, I. J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y · 2014
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Adam: A method for stochastic optimization
Kingma, D. P. and Ba, J · 2014
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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
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Stochastic backpropagation and approximate inference in deep generative models
Rezende, D. J., Mohamed, S., and Wierstra, D · 2014
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Generating sentences from a continuous space
Bowman, S. R., Vilnis, L., Vinyals, O., Dai, A. M., Jozefowicz, R., and Bengio, S · 2015
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Draw: A recurrent neural network for image generation
Gregor, K., Danihelka, I., Graves, A., Rezende, D., and Wierstra, D · 2015
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Generating images from captions with attention
Mansimov, E., Parisotto, E., Ba, J. L., and Salakhutdinov, R · 2015
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Neural machine translation of rare words with subword units
Sennrich, R., Haddow, B., and Birch, A · 2015
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Tensorflow: A system for large-scale machine learning
Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., Isard, M., et al · 2016
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Identity mappings in deep residual networks
He, K., Zhang, X., Ren, S., and Sun, J · 2016
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beta-vae: Learning basic visual concepts with a constrained variational framework
Higgins, I., Matthey, L., Pal, A., Burgess, C., Glorot, X., Botvinick, M., Mohamed, S., and Lerchner, A · 2016
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Categorical reparameterization with gumbel-softmax
Jang, E., Gu, S., and Poole, B · 2016
Cited alongside, same era.
The concrete distribution: A continuous relaxation of discrete random variables
Maddison, C. J., Mnih, A., and Teh, Y. W · 2016
Cited alongside, same era.
Improved techniques for training gans
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X · 2016
Cited alongside, same era.
Yfcc100m: The new data in multimedia research
Thomee, B., Shamma, D. A., Friedland, G., Elizalde, B., Ni, K., Poland, D., Borth, D., and Li, L.-J · 2016
Cited alongside, same era.
Andreas, J., Klein, D., and Levine, S · 2017
Stackgan++: Realistic image synthesis with stacked generative adversarial networks
Zhang, H., Xu, T., Li, H., Zhang, S., Wang, X., Huang, X., and Metaxas, D. N · 2018
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Generating long sequences with sparse transformers
Child, R., Gray, S., Radford, A., and Sutskever, I · 2019
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Object-driven text-to-image synthesis via adversarial training
Li, W., Zhang, P., Zhang, L., Huang, Q., He, X., Lyu, S., and Gao, J · 2019
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Bpe-dropout: Simple and effective subword regularization
Provilkov, I., Emelianenko, D., and Voita, E · 2019
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Language models are unsupervised multitask learners
Radford, A., Wu, J., Child, R., Luan, D., Amodei, D., and Sutskever, I · 2019
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Cited alongside, same era.
Gans trained by a two time-scale update rule converge to a local nash equilibrium
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S · 2017
Cited alongside, same era.
Image-to-image translation with conditional adversarial networks
Isola, P., Zhu, J.-Y., Zhou, T., and Efros, A. A · 2017
Cited alongside, same era.
Flexpoint: An adaptive numerical format for efficient training of deep neural networks
Köster, U., Webb, T. J., Wang, X., Nassar, M., Bansal, A. K., Constable, W. H., Elibol, O. H., Gray, S., Hall, S., Hornof, L., et al · 2017
Cited alongside, same era.
Decoupled weight decay regularization
Loshchilov, I. and Hutter, F · 2017
Cited alongside, same era.
Micikevicius, P., Narang, S., Alben, J., Diamos, G., Elsen, E., Garcia, D., Ginsburg, B., Houston, M., Kuchaiev, O., Venkatesh, G., et al · 2017
Cited alongside, same era.
Plug & play generative networks: Conditional iterative generation of images in latent space
Nguyen, A., Clune, J., Bengio, Y., Dosovitskiy, A., and Yosinski, J · 2017
Cited alongside, same era.
Neural discrete representation learning
Oord, A. v. d., Vinyals, O., and Kavukcuoglu, K · 2017
Cited alongside, same era.
Rajbhandari, S., Rasley, J., Ruwase, O., and He, Y · 2019
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Generating diverse high-fidelity images with vq-vae-2
Razavi, A., Oord, A. v. d., and Vinyals, O · 2019
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Powersgd: Practical low-rank gradient compression for distributed optimization
Vogels, T., Karimireddy, S. P., and Jaggi, M · 2019
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Generative pretraining from pixels
Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., and Sutskever, I · 2020
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X-lxmert: Paint, caption and answer questions with multi-modal transformers
Cho, J., Lu, J., Schwenk, D., Hajishirzi, H., and Kembhavi, A · 2020
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Jukebox: A generative model for music
Dhariwal, P., Jun, H., Payne, C., Kim, J. W., Radford, A., and Sutskever, I · 2020
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On the binding problem in artificial neural networks
Greff, K., van Steenkiste, S., and Schmidhuber, J · 2020
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Understanding the difficulty of training transformers
Liu, L., Liu, X., Gao, J., Chen, W., and Han, J · 2020
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Ultra-low precision 4-bit training of deep neural networks
Sun, X., Wang, N., Chen, C.-Y., Ni, J., Agrawal, A., Cui, X., Venkataramani, S., El Maghraoui, K., Srinivasan, V. V., and Gopalakrishnan, K · 2020
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Df-gan: Deep fusion generative adversarial networks for text-to-image synthesis
Tao, M., Tang, H., Wu, S., Sebe, N., Wu, F., and Jing, X.-Y · 2020
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Text-to-image generation grounded by fine-grained user attention
Koh, J. Y., Baldridge, J., Lee, H., and Yang, Y · 2021
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Learning transferable visual models from natural language supervision
Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., Krueger, G., and Sutskever, I · 2021
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