Fetching the paper…
Reading the bibliography…
Text-to-image models are becoming increasingly popular, revolutionizing the landscape of digital art creation by enabling highly detailed and creative visual content generation.
Hinton, G.E., Roweis, S.T.: Stochastic neighbor embedding. In: Neural Information Processing Systems (2002)
2002
Earlier work this paper cites.
Tjong Kim Sang, E.F., De Meulder, F.: Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition. In: Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003. pp. 142–147 (2003), https://www.aclweb.org/anthology/W03-0419
2003
Earlier work this paper cites.
Arthur, D., Vassilvitskii, S.: k-means++: the advantages of careful seeding. In: ACM-SIAM Symposium on Discrete Algorithms (2007)
2007
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
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 fourteenth international conference on artificial intelligence and statistics. pp. 215–223. JMLR Workshop and Conference Proceedings (2011)
2011
Earlier work this paper cites.
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: Proceedings of the IEEE international conference on computer vision. pp. 3730–3738 (2015)
2015
Earlier work this paper cites.
Goodfellow, I.J., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge, MA, USA (2016), http://www.deeplearningbook.org
2016
Earlier work this paper cites.
Tan, W.R., Chan, C.S., Aguirre, H.E., Tanaka, K.: Artgan: Artwork synthesis with conditional categorical gans. In: 2017 IEEE International Conference on Image Processing (ICIP). pp. 3760–3764. IEEE (2017)
2017
Earlier work this paper cites.
Zhu, J.Y., Park, T., Isola, P., Efros, A.A.: Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE international conference on computer vision. pp. 2223–2232 (2017)
2017
Earlier work this paper cites.
Karras, T., Laine, S., Aila, T.: A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 4401–4410 (2019)
2019
Earlier work this paper cites.
Zeng, X., Liu, C., Wang, Y.S., Qiu, W., Xie, L., Tai, Y.W., Tang, C.K., Yuille, A.L.: Adversarial attacks beyond the image space. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 4302–4311 (2019)
2019
Earlier work this paper cites.
2020
Earlier work this paper cites.
Hanu, L., Unitary team: Detoxify. Github. https://github.com/unitaryai/detoxify (2020)
2020
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
Ho, J., Jain, A., Abbeel, P.: Denoising diffusion probabilistic models. Advances in Neural Information Processing Systems 33
2020
Earlier work this paper cites.
Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., Aila, T.: Training generative adversarial networks with limited data. Advances in neural information processing systems 33
2020
Earlier work this paper cites.
2020
Earlier work this paper cites.
Dhariwal, P., Nichol, A.: Diffusion models beat gans on image synthesis. Advances in Neural Information Processing Systems 34
2021
Earlier work this paper cites.
2021
Earlier work this paper cites.
Kasten, Y., Ofri, D., Wang, O., Dekel, T.: Layered neural atlases for consistent video editing. ACM Transactions on Graphics (TOG) 40
2021
Earlier work this paper cites.
Srinivasan, K., Raman, K., Chen, J., Bendersky, M., Najork, M.: Wit: Wikipedia-based image text dataset for multimodal multilingual machine learning. In: Proceedings of the 44th International ACM SIGIR Conference on Research and Development in Information Retrieval. pp. 2443–2449 (2021)
2021
Earlier work this paper cites.
Ushio, A., Camacho-Collados, J.: T-NER: An all-round python library for transformer-based named entity recognition. In: Gkatzia, D., Seddah, D. (eds.) Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations. pp. 53–62. Association for Computational Linguistics, Online (Apr 2021). https://doi.org/10.18653/v1/2021.eacl-demos.7, https://aclanthology.org/2021.eacl-demos.7
2021
Cited alongside, same era.
Ushio, A., Camacho-Collados, J.: T-NER: An all-round python library for transformer-based named entity recognition. In: Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: System Demonstrations. pp. 53–62. Association for Computational Linguistics, Online (Apr 2021). https://doi.org/10.18653/v1/2021.eacl-demos.7, https://aclanthology.org/2021.eacl-demos.7
2021
Cited alongside, same era.
Kumari, N., Zhang, B., Zhang, R., Shechtman, E., Zhu, J.Y.: Multi-concept customization of text-to-image diffusion. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 1931–1941 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
2023
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2022
Cited alongside, same era.
Bar-Tal, O., Ofri-Amar, D., Fridman, R., Kasten, Y., Dekel, T.: Text2live: Text-driven layered image and video editing. In: European Conference on Computer Vision. pp. 707–723. Springer (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Ho, J., Salimans, T.: Classifier-free diffusion guidance. arXiv preprint arXiv:2207.12598 (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Lugmayr, A., Danelljan, M., Romero, A., Yu, F., Timofte, R., Van Gool, L.: Repaint: Inpainting using denoising diffusion probabilistic models. In: 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). pp. 11451–11461 (2022)
2022
Cited alongside, same era.
von Platen, P., Patil, S., Lozhkov, A., Cuenca, P., Lambert, N., Rasul, K., Davaadorj, M., Nair, D., Paul, S., Berman, W., Xu, Y., Liu, S., Wolf, T.: Diffusers: State-of-the-art diffusion models. https://github.com/huggingface/diffusers (2022)
2022
Cited alongside, same era.
2022
Cited alongside, same era.
Ruiz, N., Li, Y., Jampani, V., Pritch, Y., Rubinstein, M., Aberman, K.: Dreambooth: Fine tuning text-to-image diffusion models for subject-driven generation. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22500–22510 (2023)
2023
Later among the works it cites.
Ryu, S.: Low-rank adaptation for fast text-to-image diffusion fine-tuning (2023), https://github.com/cloneofsimo/lora
2023
Later among the works it cites.
Schramowski, P., Brack, M., Deiseroth, B., Kersting, K.: Safe latent diffusion: Mitigating inappropriate degeneration in diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 22522–22531 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Somepalli, G., Singla, V., Goldblum, M., Geiping, J., Goldstein, T.: Diffusion art or digital forgery? investigating data replication in diffusion models. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 6048–6058 (2023)
2023
Later among the works it cites.
2023
Later among the works it cites.
Zhang, L., Agrawala, M.: Adding conditional control to text-to-image diffusion models (2023)
2023
Later among the works it cites.
Zhang, L., Rao, A., Agrawala, M.: Adding conditional control to text-to-image diffusion models. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3836–3847 (2023)
2023
Later among the works it cites.
Chen, Y., Zou, J.Y.: Twigma: A dataset of ai-generated images with metadata from twitter. Advances in Neural Information Processing Systems 36
2024
Closest in time.
Funk, S.: Netflix update: Try this at home (2006), https://sifter.org/simon/journal/20061211.html , accessed: 2024-06-04
2024
Closest in time.
2024
Closest in time.
Midjourney user prompts (2022), https://www.kaggle.com/datasets/succinctlyai/midjourney-texttoimage , accessed: 2024-06-04
2024
Closest in time.
2024
Closest in time.
Somepalli, G., Gupta, A., Gupta, K., Palta, S., Goldblum, M., Geiping, J., Shrivastava, A., Goldstein, T.: Measuring style similarity in diffusion models (2024)
2024
Closest in time.
Surprise (2019), https://surpriselib.com/ , accessed: 2024-06-04
2024
Closest in time.
Matrix factorization-based algorithms (2015), https://surprise.readthedocs.io/en/v1.1.1/matrix_factorization.html#unbiased-note , accessed: 2024-06-04
2024
Closest in time.