Fetching the paper…
Reading the bibliography…
Although current image generation methods have reached impressive quality levels, they are still unable to produce plausible yet diverse images of handwritten words.
Marti, U.V., Bunke, H.: The IAM-database: an English sentence database for offline handwriting recognition. International Journal on Document Analysis and Recognition 5
2002
Earlier work this paper cites.
Wang, J., Wu, C., Xu, Y.Q., Shum, H.Y.: Combining shape and physical models for online cursive handwriting synthesis. International Journal of Document Analysis and Recognition 7
2005
Earlier work this paper cites.
Konidaris, T., Gatos, B., Ntzios, K., Pratikakis, I., Theodoridis, S., Perantonis, S.J.: Keyword-guided word spotting in historical printed documents using synthetic data and user feedback. International Journal of Document Analysis and Recognition 9
2007
Earlier work this paper cites.
Lin, Z., Wan, L.: Style-preserving English handwriting synthesis. Pattern Recognition 40
2007
Earlier work this paper cites.
Maaten, L.v.d., Hinton, G.: Visualizing data using t-SNE. Journal of Machine Learning Research 9
2008
Earlier work this paper cites.
Bird, S., Klein, E., Loper, E.: Natural language processing with Python: analyzing text with the natural language toolkit. O’Reilly Media, Inc. (2009)
2009
Earlier work this paper cites.
Thomas, A.O., Rusu, A., Govindaraju, V.: Synthetic handwritten CAPTCHAs. Pattern Recognition 42
2009
Earlier work this paper cites.
2013
Earlier work this paper cites.
Chung, J., Gulcehre, C., Cho, K., Bengio, Y.: Empirical evaluation of gated recurrent neural networks on sequence modeling. In: Proceedings of the NeurIPS Workshop on Deep Learning (2014)
2014
Earlier work this paper cites.
Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., Bengio, Y.: Generative adversarial nets. In: Proceedings of the Neural Information Processing Systems Conference (2014)
2014
Earlier work this paper cites.
Kingma, D.P., Welling, M.: Auto-encoding variational Bayes. In: Proceedings of the International Conference on Learning Representations (2014)
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
Gregor, K., Danihelka, I., Graves, A., Rezende, D.J., Wierstra, D.: DRAW: A recurrent neural network for image generation. In: Proceedings of the International Conference on Machine Learning (2015)
2015
Earlier work this paper cites.
Ioffe, S., Szegedy, C.: Batch normalization: Accelerating deep network training by reducing internal covariate shift. In: Proceedings of the International Conference on Machine Learning (2015)
2015
Earlier work this paper cites.
Simonyan, K., Zisserman, A.: Very deep convolutional networks for large-scale image recognition. In: Proceedings of the International Conference on Learning Representations (2015)
2015
Earlier work this paper cites.
Haines, T.S., Mac Aodha, O., Brostow, G.J.: My text in your handwriting. ACM Transactions on Graphics 35
2016
Earlier work this paper cites.
2016
Earlier work this paper cites.
Haines, T.S., Mac Aodha, O., Brostow, G.J.: My text in your handwriting. ACM Transactions on Graphics 35
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: Proceedings of the Neural Information Processing Systems Conference (2017)
2017
Cited alongside, same era.
Huang, X., Belongie, S.: Arbitrary style transfer in real-time with adaptive instance normalization. In: Proceedings of the IEEE International Conference on Computer Vision (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 Conference on Computer Vision and Pattern Recognition (2017)
2017
Cited alongside, same era.
Tulyakov, S., Liu, M.Y., Yang, X., Kautz, J.: MoCoGAN: Decomposing motion and content for video generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
2018
Later among the works it cites.
Alonso, E., Moysset, B., Messina, R.: Adversarial generation of handwritten text images conditioned on sequences. In: Proceedings of the International Conference on Document Analysis and Recognition (2019)
2019
Later among the works it cites.
Borji, A.: Pros and cons of GAN evaluation measures. Computer Vision and Image Understanding 179
2019
Later among the works it cites.
Brock, A., Donahue, J., Simonyan, K.: Large scale GAN training for high fidelity natural image synthesis. In: Proceedings of the International Conference on Learning Representations (2019)
2019
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Lyu, P., Bai, X., Yao, C., Zhu, Z., Huang, T., Liu, W.: Auto-encoder guided GAN for Chinese calligraphy synthesis. In: Proceedings of the International Conference on Document Analysis and Recognition (2017)
2017
Cited alongside, same era.
Odena, A., Olah, C., Shlens, J.: Conditional image synthesis with auxiliary classifier GANs. In: Proceedings of the International Conference on Machine Learning (2017)
2017
Cited alongside, same era.
Taigman, Y., Polyak, A., Wolf, L.: Unsupervised cross-domain image generation. In: Proceedings of the International Conference on Learning Representations (2017)
2017
Cited alongside, same era.
Tian, Y.: zi2zi: Master chinese calligraphy with conditional adversarial networks (2017), https://github.com/kaonashi-tyc/zi2zi
2017
Cited alongside, same era.
Yu, L., Zhang, W., Wang, J., Yu, Y.: SeqGAN: Sequence generative adversarial nets with policy gradient. In: Proceedings of the AAAI Conference on Artificial Intelligence (2017)
2017
Cited alongside, same era.
Azadi, S., Fisher, M., Kim, V.G., Wang, Z., Shechtman, E., Darrell, T.: Multi-content gan for few-shot font style transfer. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 7564–7573 (2018)
2018
Cited alongside, same era.
Chang, B., Zhang, Q., Pan, S., Meng, L.: Generating handwritten Chinese characters using CycleGAN. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision (2018)
2018
Cited alongside, same era.
Choi, Y., Choi, M., Kim, M., Ha, J.W., Kim, S., Choo, J.: StarGAN: Unified generative adversarial networks for multi-domain image-to-image translation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2018)
2018
Cited alongside, same era.
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (2019)
2019
Later among the works it cites.
Liu, M.Y., Huang, X., Mallya, A., Karras, T., Aila, T., Lehtinen, J., Kautz, J.: Few-shot unsupervised image-to-image translation. In: Proceedings of the IEEE International Conference on Computer Vision (2019)
2019
Later among the works it cites.
2019
Later among the works it cites.
Pondenkandath, V., Alberti, M., Diatta, M., Ingold, R., Liwicki, M.: Historical document synthesis with generative adversarial networks. In: Proceedings of the International Conference on Document Analysis and Recognition (2019)
2019
Later among the works it cites.
Zhan, F., Xue, C., Lu, S.: Ga-dan: Geometry-aware domain adaptation network for scene text detection and recognition. In: Proceedings of the IEEE International Conference on Computer Vision. pp. 9105–9115 (2019)
2019
Later among the works it cites.
Zhan, F., Zhu, H., Lu, S.: Spatial fusion gan for image synthesis. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 3653–3662 (2019)
2019
Later among the works it cites.
Zheng, N., Jiang, Y., Huang, D.: Strokenet: A neural painting environment. In: Proceedings of the International Conference on Learning Representations (2019)
2019
Later among the works it cites.
Alonso, E., Moysset, B., Messina, R.: Adversarial generation of handwritten text images conditioned on sequences. In: Proceedings of the International Conference on Document Analysis and Recognition (2019)
2019
Later among the works it cites.
Fogel, S., Averbuch-Elor, H., Cohen, S., Mazor, S., Litman, R.: Scrabblegan: Semi-supervised varying length handwritten text generation. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. pp. 4324–4333 (2020)
2020
Closest in time.
Kang, L., Rusiñol, M., Fornés, A., Riba, P., Villegas, M.: Unsupervised adaptation for synthetic-to-real handwritten word recognition. In: Proceedings of the IEEE Winter Conference on Applications of Computer Vision. pp. 3491–3500 (2020)
2020
Closest in time.
2020
Closest in time.