| Attngan: Fine-grained text to image generation with attentional generative adversarial networks T Xu, P Zhang, Q Huang, H Zhang, Z Gan, X Huang, X He Proceedings of the IEEE conference on computer vision and pattern …, 2018 | 505 | 2018 |
| Variational Autoencoder for Deep Learning of Images, Labels and Captions Y Pu, Z Gan, R Henao, X Yuan, C Li, A Stevens, L Carin NIPS, 2016 | 443 | 2016 |
| Semantic compositional networks for visual captioning Z Gan, C Gan, X He, Y Pu, K Tran, J Gao, L Carin, L Deng Proceedings of the IEEE conference on computer vision and pattern …, 2017 | 269 | 2017 |
| Adversarial feature matching for text generation Y Zhang, Z Gan, K Fan, Z Chen, R Henao, D Shen, L Carin arXiv preprint arXiv:1706.03850, 2017 | 217 | 2017 |
| Stylenet: Generating attractive visual captions with styles C Gan, Z Gan, X He, J Gao, L Deng Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2017 | 143 | 2017 |
| Uniter: Learning universal image-text representations YC Chen, L Li, L Yu, AE Kholy, F Ahmed, Z Gan, Y Cheng, J Liu arXiv preprint arXiv:1909.11740, 2019 | 128* | 2019 |
| Patient knowledge distillation for bert model compression S Sun, Y Cheng, Z Gan, J Liu arXiv preprint arXiv:1908.09355, 2019 | 127 | 2019 |
| Generating informative and diverse conversational responses via adversarial information maximization Y Zhang, M Galley, J Gao, Z Gan, X Li, C Brockett, B Dolan Advances in Neural Information Processing Systems 31, 1810-1820, 2018 | 113 | 2018 |
| Generating Text via Adversarial Training Y Zhang, Z Gan, L Carin Workshop on Adversarial Training, NIPS 2016, 2016 | 112 | 2016 |
| Learning deep sigmoid belief networks with data augmentation Z Gan, R Henao, D Carlson, L Carin Artificial Intelligence and Statistics, 268-276, 2015 | 107 | 2015 |
| Learning Generic Sentence Representations Using Convolutional Neural Networks Z Gan, Y Pu, R Henao, C Li, X He, L Carin Conference on Empirical Methods in Natural Language Processing (EMNLP), 2017 | 104* | 2017 |
| Triangle generative adversarial networks Z Gan, L Chen, W Wang, Y Pu, Y Zhang, H Liu, C Li, L Carin Advances in neural information processing systems, 5247-5256, 2017 | 96 | 2017 |
| Scalable Deep Poisson Factor Analysis for Topic Modeling Z Gan, C Chen, R Henao, D Carlson, L Carin Proceedings of the 32nd International Conference on Machine Learning, 2015 | 78 | 2015 |
| Deep temporal sigmoid belief networks for sequence modeling Z Gan, C Li, R Henao, DE Carlson, L Carin Advances in Neural Information Processing Systems 28, 2467-2475, 2015 | 76 | 2015 |
| Deconvolutional paragraph representation learning Y Zhang, D Shen, G Wang, Z Gan, R Henao, L Carin Advances in Neural Information Processing Systems, 4169-4179, 2017 | 74 | 2017 |
| Adversarial text generation via feature-mover's distance L Chen, S Dai, C Tao, H Zhang, Z Gan, D Shen, Y Zhang, G Wang, ... Advances in Neural Information Processing Systems, 4666-4677, 2018 | 64 | 2018 |
| Relation-aware graph attention network for visual question answering L Li, Z Gan, Y Cheng, J Liu Proceedings of the IEEE International Conference on Computer Vision, 10313-10322, 2019 | 63 | 2019 |
| Adversarial Symmetric Variational Autoencoder Y Pu, W Wang, R Henao, L Chen, Z Gan, C Li, L Carin Neural and Information Processing Systems (NIPS), 2017 | 62 | 2017 |
| Bridging the gap between stochastic gradient MCMC and stochastic optimization C Chen, D Carlson, Z Gan, C Li, L Carin Artificial Intelligence and Statistics, 1051-1060, 2016 | 62 | 2016 |
| Freelb: Enhanced adversarial training for natural language understanding C Zhu, Y Cheng, Z Gan, S Sun, T Goldstein, J Liu International Conference on Learning Representations, 2020 | 61* | 2020 |
Lawrence CarinProfessor of Electrical and Computer Engineering, Duke UniversityVerified email at ece.duke.edu
