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Transformer models have recently attracted much interest from computer vision researchers and have since been successfully employed for several problems traditionally addressed with convolutional neural networks.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: Proceedings of the IEEE Computer Vision and Pattern Recognition. pp. 248–255 (2009)
2009
Earlier work this paper cites.
Krizhevsky, A., Hinton, G., et al.: Learning multiple layers of features from tiny images. Tech. rep., University of Toronto (2009)
2009
Earlier work this paper cites.
Nair, V., Hinton, G.E.: Rectified linear units improve restricted boltzmann machines. In: International Conference on Machine Learning (2010)
2010
Earlier work this paper cites.
Coates, A., Ng, A., Lee, H.: An analysis of single-layer networks in unsupervised feature learning. In: Proceedings of the International Conference on Artificial Intelligence and Statistics. pp. 215–223 (2011)
2011
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational bayes. In: International Conference on Learning Representations (2013)
2013
Earlier work this paper cites.
Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial networks. In: Advances in Neural Information Processing Systems (2014)
2014
Earlier work this paper cites.
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. In: International Conference on Learning Representations (2015)
2015
Earlier work this paper cites.
Ba, J.L., Kiros, J.R., Hinton, G.E.: Layer normalization. arXiv:1607.06450 (2016)
2016
Earlier work this paper cites.
Hendrycks, D., Gimpel, K.: Gaussian error linear units (gelus). arXiv:1606.08415 (2016)
2016
Earlier work this paper cites.
Oord, A.v.d., Kalchbrenner, N., Vinyals, O., Espeholt, L., Graves, A., Kavukcuoglu, K.: Conditional image generation with pixelcnn decoders. In: Advances in Neural Information Processing Systems (2016)
2016
Earlier work this paper cites.
Reed, S., Akata, Z., Yan, X., Logeswaran, L., Schiele, B., Lee, H.: Generative adversarial text to image synthesis. In: International Conference on Machine Learning. pp. 1060–1069 (2016)
2016
Earlier work this paper cites.
Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., Chen, X.: Improved techniques for training gans. In: Advances in Neural Information Processing Systems (2016)
2016
Earlier work this paper cites.
Shi, W., Caballero, J., Huszár, F., Totz, J., Aitken, A.P., Bishop, R., Rueckert, D., Wang, Z.: Real-time single image and video super-resolution using an efficient sub-pixel convolutional neural network. In: Proceedings of the IEEE Computer Vision and Pattern Recognition. pp. 1874–1883 (2016)
2016
Earlier work this paper cites.
Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., Wojna, Z.: Rethinking the inception architecture for computer vision. In: Proceedings of the IEEE Computer Vision and Pattern Recognition. pp. 2818–2826 (2016)
2016
Earlier work this paper cites.
Van Oord, A., Kalchbrenner, N., Kavukcuoglu, K.: Pixel recurrent neural networks. In: International Conference on Machine Learning. pp. 1747–1756 (2016)
2016
Cited alongside, same era.
Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., Hochreiter, S.: Gans trained by a two time-scale update rule converge to a local nash equilibrium. In: Advances in Neural Information Processing Systems (2017)
2017
Cited alongside, same era.
Isola, P., Zhu, J.Y., Zhou, T., Efros, A.A.: Image-to-image translation with conditional adversarial networks. In: Proceedings of the IEEE Computer Vision and Pattern Recognition. pp. 1125–1134 (2017)
2017
Cited alongside, same era.
Ledig, C., Theis, L., Huszár, F., Caballero, J., Cunningham, A., Acosta, A., Aitken, A., Tejani, A., Totz, J., Wang, Z., et al.: Photo-realistic single image super-resolution using a generative adversarial network. In: Proceedings of the IEEE Computer Vision and Pattern Recognition. pp. 4681–4690 (2017)
2017
Brown, T.B., Mann, B., Ryder, N., Subbiah, M., Kaplan, J., Dhariwal, P., Neelakantan, A., Shyam, P., Sastry, G., Askell, A., et al.: Language models are few-shot learners. In: Advances in Neural Information Processing Systems (2020)
2020
Later among the works it cites.
Carion, N., Massa, F., Synnaeve, G., Usunier, N., Kirillov, A., Zagoruyko, S.: End-to-end object detection with transformers. In: European Conference on Computer Vision. pp. 213–229 (2020)
2020
Later among the works it cites.
Chen, M., Radford, A., Child, R., Wu, J., Jun, H., Luan, D., Sutskever, I.: Generative pretraining from pixels. In: International Conference on Machine Learning. pp. 1691–1703 (2020)
2020
Later among the works it cites.
Durall, R., Keuper, M., Keuper, J.: Watch your up-convolution: Cnn based generative deep neural networks are failing to reproduce spectral distributions. In: Proceedings of the IEEE Computer Vision and Pattern Recognition. pp. 7890–7899 (2020)
2020
Later among the works it cites.
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Cited alongside, same era.
Oord, A.v.d., Vinyals, O., Kavukcuoglu, K.: Neural discrete representation learning. In: Advances in Neural Information Processing Systems (2017)
2017
Cited alongside, same era.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L., Polosukhin, I.: Attention is all you need. In: Advances in Neural Information Processing Systems. pp. 5998–6008 (2017)
2017
Cited alongside, same era.
Brock, A., Donahue, J., Simonyan, K.: Large scale gan training for high fidelity natural image synthesis. In: International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
Miyato, T., Kataoka, T., Koyama, M., Yoshida, Y.: Spectral normalization for generative adversarial networks. In: International Conference on Learning Representations (2018)
2018
Cited alongside, same era.
Parmar, N., Vaswani, A., Uszkoreit, J., Kaiser, L., Shazeer, N., Ku, A., Tran, D.: Image transformer. In: International Conference on Machine Learning. pp. 4055–4064 (2018)
2018
Cited alongside, same era.
2019
Cited alongside, same era.
Gong, X., Chang, S., Jiang, Y., Wang, Z.: Autogan: Neural architecture search for generative adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 3224–3234 (2019)
2019
Cited alongside, same era.
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE Computer Vision and Pattern Recognition. pp. 4401–4410 (2019)
2019
Cited alongside, same era.
2020
Later among the works it cites.
Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., Aila, T.: Analyzing and improving the image quality of stylegan. In: Proceedings of the IEEE Computer Vision and Pattern Recognition. pp. 8110–8119 (2020)
2020
Later among the works it cites.
Lee, C.H., Liu, Z., Wu, L., Luo, P.: Maskgan: Towards diverse and interactive facial image manipulation. In: Proceedings of the IEEE Computer Vision and Pattern Recognition. pp. 5549–5558 (2020)
2020
Later among the works it cites.
Dosovitskiy, A., Beyer, L., Kolesnikov, A., Weissenborn, D., Zhai, X., Unterthiner, T., Dehghani, M., Minderer, M., Heigold, G., Gelly, S., et al.: An image is worth 16x16 words: Transformers for image recognition at scale. In: International Conference on Learning Representations (2021)
2021
Closest in time.
2021
Closest in time.
2021
Closest in time.
Hudson, D.A., Zitnick, C.L.: Generative adversarial transformers. arXiv:2103.01209 (2021)
2021
Closest in time.
2021
Closest in time.