| CAN: Creative adversarial networks, generating" art" by learning about styles and deviating from style norms A Elgammal, B Liu, M Elhoseiny, M Mazzone Ninth International Conference on Computational Creativity, 2017 | 749* | 2017 |
| A Generative Adversarial Approach for Zero-Shot Learning from Noisy Texts Y Zhu, M Elhoseiny, B Liu, X Peng, A Elgammal CVPR 2018, 1004-1013, 2018 | 471 | 2018 |
| Towards Faster and Stabilized GAN Training for High-fidelity Few-shot Image Synthesis B Liu, Y Zhu, K Song, A Elgammal ICLR-2021, arXiv: 2101.04775, 2021 | 250 | 2021 |
| The Shape of Art History in the Eyes of the Machine A Elgammal, B Liu, D Kim, M Elhoseiny, M Mazzone AAAI 2018, 2018 | 152 | 2018 |
| Learning feature-to-feature translator by alternating back-propagation for generative zero-shot learning Y Zhu, J Xie, B Liu, A Elgammal ICCV 2019, 9844-9854, 2019 | 115 | 2019 |
| Common Diffusion Noise Schedules and Sample Steps are Flawed S Lin, B Liu, J Li, X Yang WACV 2024, 2023 | 89 | 2023 |
| OOGAN: Disentangling GAN with One-Hot Sampling and Orthogonal Regularization B Liu, Y Zhu, Z Fu, G de Melo, A Elgammal AAAI 2020, 2019 | 47 | 2019 |
| Self-Supervised Sketch-to-Image Synthesis B Liu, Y Zhu, K Song, A Elgammal AAAI 2021, 2020 | 42 | 2020 |
| Sketch-to-Art: Synthesizing Stylized Art Images From Sketches B Liu, K Song, A Elgammal ACCV 2020 & SIGGRAPH 2020 Real-Time Live!, 2020 | 40 | 2020 |
| Shifted Diffusion for Text-to-image Generation Z Yufan, L Bingchen, Z Yizhe, Y Xiao, C Changyou, X Jinhui CVPR 2023, https://arxiv.org/abs/2211.15388, 2022 | 33* | 2022 |
| TIME: Text and Image Mutual-Translation Adversarial Networks B Liu, K Song, Y Zhu, G de Melo, A Elgammal AAAI 2021, 2020 | 31 | 2020 |
| Diffusion guided domain adaptation of image generators K Song, L Han, B Liu, D Metaxas, A Elgammal WACV 2024, 2022 | 23 | 2022 |
| Finding principal semantics of style in art D Kim, B Liu, A Elgammal, M Mazzone 2018 IEEE 12th International Conference on Semantic Computing (ICSC), 156-163, 2018 | 19 | 2018 |
| Stylegan-fusion: Diffusion guided domain adaptation of image generators K Song, L Han, B Liu, D Metaxas, A Elgammal Proceedings of the IEEE/CVF Winter Conference on Applications of Computer …, 2024 | 4 | 2024 |
| MoMA: Multimodal LLM Adapter for Fast Personalized Image Generation K Song, Y Zhu, B Liu, Q Yan, A Elgammal, X Yang arXiv preprint arXiv:2404.05674, 2024 | 1 | 2024 |
| Generation of images corresponding to input text using multi-algorithm diffusion sampling Q Yan, LIU Bingchen, Y Zhu, X Yang US Patent App. 18/052,862, 2024 | | 2024 |
| Generation of image corresponding to input text using multi-text guided image cropping LIU Bingchen, Y Zhu, X Yang US Patent App. 18/052,870, 2024 | | 2024 |
| Generation of image corresponding to input text using dynamic value clipping LIU Bingchen, Y Zhu, X Yang US Patent App. 18/052,866, 2024 | | 2024 |
| Generation of curated training data for diffusion models LIU Bingchen, Y Zhu, X Yang US Patent App. 18/052,865, 2024 | | 2024 |
| Disentangled Generative Models and Their Applications B Liu Rutgers The State University of New Jersey, School of Graduate Studies, 2022 | | 2022 |
Ahmed ElgammalRutgers University在 cs.rutgers.edu 的电子邮件经过验证
