The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient
Open accessAbstract
As a new way of training generative models, Generative Adversarial Net (GAN) that uses a discriminative model to guide the training of the generative model has enjoyed considerable success in generating real-valued data. However, it has limitations when the goal is for generating sequences of discrete tokens. A major reason lies in that the discrete outputs from the generative model make it difficult to pass the gradient update from the discriminative model to the generative model. Also, the discriminative model can only assess a complete sequence, while for a partially generated sequence, it is non-trivial to balance its current score and the future one once the entire sequence has been generated. In this paper, we propose a sequence generation framework, called SeqGAN, to solve the problems. Modeling the data generator as a stochastic policy in reinforcement learning (RL), SeqGAN bypasses the generator differentiation problem by directly performing gradient policy update. The RL reward signal comes from the GAN discriminator judged on a complete sequence, and is passed back to the intermediate state-action steps using Monte Carlo search. Extensive experiments on synthetic data and real-world tasks demonstrate significant improvements over strong baselines.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cites
Hochreiter, S., & Schmidhuber, J. (1997) · Neural Computation · doi:10.1162/neco.1997.9.8.1735 · Cited by 102,318
Auto-Encoding Variational Bayes
Kingma, D. P., & Welling, M. (2013) · arXiv · doi:10.48550/arxiv.1312.6114 · Cited by 20,067
A fast learning algorithm for deep belief nets
Hinton, G. E., Osindero, S., & Teh, Y.-W. (2006) · Neural Computation · doi:10.1162/neco.2006.18.7.1527 · Cited by 16,336
Mastering the game of Go with deep neural networks and tree search
Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., van den Driessche, G., Schrittwieser, J., Antonoglou, I., Panneershelvam, V., Lanctot, M., et al. (2016) · Nature · doi:10.1038/nature16961 · Cited by 15,612
Scheduled Sampling for Sequence Prediction with Recurrent Neural Networks
Bengio, S., Vinyals, O., Jaitly, N., & Shazeer, N. (2015) · arXiv · doi:10.48550/arxiv.1506.03099 · Cited by 1,621
Sequence Level Training with Recurrent Neural Networks
Ranzato, M., Chopra, S., Auli, M., & Zaremba, W. (2015) · arXiv · doi:10.48550/arxiv.1511.06732 · Cited by 1,426
An Actor-Critic Algorithm for Sequence Prediction
Bahdanau, D., Brakel, P., Xu, K., Goyal, A., Lowe, R., Pineau, J., Courville, A., & Bengio, Y. (2016) · arXiv · doi:10.48550/arxiv.1607.07086 · Cited by 423
Chinese Poetry Generation with Recurrent Neural Networks
Zhang, X., & Lapata, M. (2014) · Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP) · doi:10.3115/v1/d14-1074 · Cited by 298
Cited by
Physics-informed machine learning
Karniadakis, G. E., Kevrekidis, I. G., Lu, L., Perdikaris, P., Wang, S., & Yang, L. (2021) · Nature Reviews Physics · doi:10.1038/s42254-021-00314-5 · Cited by 8,718
Improved training of Wasserstein GANs
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., & Courville, A. (2017) · arXiv · doi:10.48550/arxiv.1704.00028 · Cited by 6,800
Recent Trends in Deep Learning Based Natural Language Processing [Review Article]
Young, T., Hazarika, D., Poria, S., & Cambria, E. (2018) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2018.2840738 · Cited by 2,921
ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators
Clark, K., Luong, M.-T., Le, Q. V., & Manning, C. D. (2020) · arXiv · doi:10.48550/arxiv.2003.10555 · Cited by 1,844
The Curious Case of Neural Text Degeneration
Holtzman, A., Buys, J., Du, L., Forbes, M., & Choi, Y. (2019) · arXiv · doi:10.48550/arxiv.1904.09751 · Cited by 1,304
A Review on Generative Adversarial Networks: Algorithms, Theory, and Applications
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2023) · IEEE Transactions on Knowledge and Data Engineering · doi:10.1109/tkde.2021.3130191 · Cited by 1,231
A Comprehensive Survey of Deep Learning for Image Captioning
Hossain, M. Z., Sohel, F., Shiratuddin, M. F., & Laga, H. (2019) · ACM Computing Surveys · doi:10.1145/3295748 · Cited by 879
Molecular Sets (MOSES): A Benchmarking Platform for Molecular Generation Models
Polykovskiy, D., Zhebrak, A., Sanchez-Lengeling, B., Golovanov, S., Tatanov, O., Belyaev, S., Kurbanov, R., Artamonov, A., Aladinskiy, V., Veselov, M., et al. (2020) · Frontiers in Pharmacology · doi:10.3389/fphar.2020.565644 · Cited by 849
Deep Reinforcement Learning: An Overview
Li, Y. (2017) · arXiv · doi:10.48550/arxiv.1701.07274 · Cited by 762
Deep learning for molecular design—a review of the state of the art
Elton, D. C., Boukouvalas, Z., Fuge, M. D., & Chung, P. W. (2019) · Molecular Systems Design & Engineering · doi:10.1039/c9me00039a · Cited by 683
Plug and Play Language Models: A Simple Approach to Controlled Text Generation
Dathathri, S., Madotto, A., Lan, J., Hung, J., Frank, E., Molino, P., Yosinski, J., & Liu, R. (2019) · arXiv · doi:10.48550/arxiv.1912.02164 · Cited by 508
(2020) · arXiv (Cornell University) · Cited by 461
Improved Image Captioning via Policy Gradient optimization of SPIDEr
Liu, S., Zhu, Z., Ye, N., Guadarrama, S., & Murphy, K. (2017) · 2017 IEEE International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2017.100 · Cited by 430
MolGAN: An implicit generative model for small molecular graphs
De Cao, N., & Kipf, T. (2018) · arXiv · doi:10.48550/arxiv.1805.11973 · Cited by 442
Texygen: A Benchmarking Platform for Text Generation Models
Zhu, Y., Lu, S., Zheng, L., Guo, J., Zhang, W., Wang, J., & Yu, Y. (2018) · The 41st International ACM SIGIR Conference on Research & Development in Information Retrieval · doi:10.1145/3209978.3210080 · Cited by 408
Self-citationC-RNN-GAN: Continuous recurrent neural networks with adversarial training
Mogren, O. (2016) · arXiv · doi:10.48550/arxiv.1611.09904 · Cited by 386
Adversarial Machine Learning Attacks and Defense Methods in the Cyber Security Domain
Rosenberg, I., Shabtai, A., Elovici, Y., & Rokach, L. (2022) · ACM Computing Surveys · doi:10.1145/3453158 · Cited by 295
Controllable Abstractive Summarization
Fan, A., Grangier, D., & Auli, M. (2018) · Proceedings of the 2nd Workshop on Neural Machine Translation and Generation · doi:10.18653/v1/w18-2706 · Cited by 281
Neural Text Generation with Unlikelihood Training
Welleck, S., Kulikov, I., Roller, S., Dinan, E., Cho, K., & Weston, J. (2019) · arXiv · doi:10.48550/arxiv.1908.04319 · Cited by 254
Deep Reinforcement Learning for Sequence-to-Sequence Models
Keneshloo, Y., Shi, T., Ramakrishnan, N., & Reddy, C. K. (2019) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2019.2929141 · Cited by 230
GeDi: Generative Discriminator Guided Sequence Generation
Krause, B., Gotmare, A. D., McCann, B., Keskar, N. S., Joty, S., Socher, R., & Rajani, N. F. (2021) · Findings of the Association for Computational Linguistics: EMNLP 2021 · doi:10.18653/v1/2021.findings-emnlp.424 · Cited by 220
A survey on neural speech synthesis
Tan, X., Qin, T., Soong, F., & Liu, T.-Y. (2021) · arXiv · doi:10.48550/arxiv.2106.15561 · Cited by 216
Machine-Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Crothers, E. N., Japkowicz, N., & Viktor, H. L. (2023) · IEEE Access · doi:10.1109/access.2023.3294090 · Cited by 196
Learning Deep Network Representations with Adversarially Regularized Autoencoders
Yu, W., Zheng, C., Cheng, W., Aggarwal, C. C., Song, D., Zong, B., Chen, H., & Wang, W. (2018) · Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining · doi:10.1145/3219819.3220000 · Cited by 188
Learning to Write with Cooperative Discriminators
Holtzman, A., Buys, J., Forbes, M., Bosselut, A., Golub, D., & Choi, Y. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1152 · Cited by 192
Privacy Risks of General-Purpose Language Models
Pan, X., Zhang, M., Ji, S., & Yang, M. (2020) · 2020 IEEE Symposium on Security and Privacy (SP) · doi:10.1109/sp40000.2020.00095 · Cited by 188
Survey on reinforcement learning for language processing
Uc-Cetina, V., Navarro-Guerrero, N., Martin-Gonzalez, A., Weber, C., & Wermter, S. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-022-10205-5 · Cited by 177
KBGAN: Adversarial Learning for Knowledge Graph Embeddings
Cai, L., & Wang, W. Y. (2018) · Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers) · doi:10.18653/v1/n18-1133 · Cited by 164
SenseGen: A deep learning architecture for synthetic sensor data generation
Alzantot, M., Chakraborty, S., & Srivastava, M. (2017) · 2017 IEEE International Conference on Pervasive Computing and Communications Workshops (PerCom Workshops) · doi:10.1109/percomw.2017.7917555 · Cited by 153
Tang, X., Yao, H., Sun, Y., Aggarwal, C., Mitra, P., & Wang, S. (2020) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v34i04.6056 · Cited by 125
On the creativity of large language models
Franceschelli, G., & Musolesi, M. (2025) · AI & SOCIETY · doi:10.1007/s00146-024-02127-3 · Cited by 113
Autonomous Molecular Design: Then and Now
Dimitrov, T., Kreisbeck, C., Becker, J. S., Aspuru-Guzik, A., & Saikin, S. K. (2019) · ACS Applied Materials & Interfaces · doi:10.1021/acsami.9b01226 · Cited by 103
Technical and clinical overview of deep learning in radiology
Ueda, D., Shimazaki, A., & Miki, Y. (2019) · Japanese Journal of Radiology · doi:10.1007/s11604-018-0795-3 · Cited by 100
Wang, J., Chen, Y., Gu, Y., Xiao, Y., & Pan, H. (2018) · 2018 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489106 · Cited by 92
Zou, W., Sun, Y., Zhou, Y., Lu, Q., Nie, Y., Sun, T., & Peng, L. (2022) · IEEE Intelligent Transportation Systems Magazine · doi:10.1109/mits.2020.2970185 · Cited by 84
Caccia, M., Caccia, L., Fedus, W., Larochelle, H., Pineau, J., & Charlin, L. (2018) · arXiv · doi:10.48550/arxiv.1811.02549 · Cited by 83
Verkhivker, G. M., Agajanian, S., Hu, G., & Tao, P. (2020) · Frontiers in Molecular Biosciences · doi:10.3389/fmolb.2020.00136 · Cited by 76
On Accurate Evaluation of GANs for Language Generation
Semeniuta, S., Severyn, A., & Gelly, S. (2018) · arXiv · doi:10.48550/arxiv.1806.04936 · Cited by 76
Shilling Black-Box Recommender Systems by Learning to Generate Fake User Profiles
Lin, C., Chen, S., Zeng, M., Zhang, S., Gao, M., & Li, H. (2024) · IEEE Transactions on Neural Networks and Learning Systems · doi:10.1109/tnnls.2022.3183210 · Cited by 62
Evaluating the Evaluation of Diversity in Natural Language Generation
Tevet, G., & Berant, J. (2021) · Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume · doi:10.18653/v1/2021.eacl-main.25 · Cited by 52
Generation of Synthetic Electronic Medical Record Text
Guan, J., Li, R., Yu, S., & Zhang, X. (2018) · 2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM) · doi:10.1109/bibm.2018.8621223 · Cited by 49
Teaching Machines to Ask Questions
Yao, K., Zhang, L., Luo, T., Tao, L., & Wu, Y. (2018) · Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence · doi:10.24963/ijcai.2018/632 · Cited by 44
Jointly Measuring Diversity and Quality in Text Generation Models
Alihosseini, D., Montahaei, E., & Soleymani Baghshah, M. (2019) · Proceedings of the Workshop on Methods for Optimizing and Evaluating Neural Language Generation · doi:10.18653/v1/w19-2311 · Cited by 48
Kreutzer, J., Uyheng, J., & Riezler, S. (2018) · Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) · doi:10.18653/v1/p18-1165 · Cited by 38
Synthetic Sensor Data for Human Activity Recognition
Alharbi, F., Ouarbya, L., & Ward, J. A. (2020) · 2020 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn48605.2020.9206624 · Cited by 40
Text-To-Text Generative Adversarial Networks
Li, C., Su, Y., & Liu, W. (2018) · 2018 International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn.2018.8489624 · Cited by 33
(2019) · doi:10.1609/aaai.v33i01.33016618 · Cited by 31
Kwon, H., Abowd, G. D., & Plötz, T. (2021) · Sensors · doi:10.3390/s21248337 · Cited by 30
Reinforcement learning and bandits for speech and language processing: Tutorial, review and outlook
Lin, B. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.122254 · Cited by 28
On the Weaknesses of Reinforcement Learning for Neural Machine Translation
Choshen, L., Fox, L., Aizenbud, Z., & Abend, O. (2019) · arXiv · doi:10.48550/arxiv.1907.01752 · Cited by 28
Liang, C., Yang, X., Wham, D., Pursel, B., Passonneaur, R., & Giles, C. L. (2017) · Proceedings of the Knowledge Capture Conference · doi:10.1145/3148011.3154463 · Cited by 23
Marchesi, R., Micheletti, N., I-Hsien Kuo, N., Barbieri, S., Jurman, G., & Osmani, V. (2025) · PLOS Computational Biology · doi:10.1371/journal.pcbi.1013080 · Cited by 29
Chinese medicine prescription recommendation using generative adversarial network
Rong, C., Li, X., Sun, X., & Sun, H. (2022) · IEEE Access · doi:10.1109/access.2022.3143797 · Cited by 17
Question Generation With Doubly Adversarial Nets
Bao, J., Gong, Y., Duan, N., Zhou, M., & Zhao, T. (2018) · IEEE/ACM Transactions on Audio, Speech, and Language Processing · doi:10.1109/taslp.2018.2859777 · Cited by 14
Cyberbully Detection Using BERT with Augmented Texts
Guo, X., Anjum, U., & Zhan, J. (2022) · 2022 IEEE International Conference on Big Data (Big Data) · doi:10.1109/bigdata55660.2022.10020581 · Cited by 12
Evaluating Text GANs as Language Models
Tevet, G., Habib, G., Shwartz, V., & Berant, J. (2019) · Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers) · doi:10.18653/v1/n19-1233 · Cited by 12
Guiding Visual Question Generation
Vedd, N., Wang, Z., Rei, M., Miao, Y., & Specia, L. (2022) · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies · doi:10.18653/v1/2022.naacl-main.118 · Cited by 12
D2C: Diffusion-Denoising Models for Few-shot Conditional Generation
Sinha, A., Song, J., Meng, C., & Ermon, S. (2021) · arXiv · doi:10.48550/arxiv.2106.06819 · Cited by 10
Detecting and Understanding Textual Deepfakes in Online Reviews
Kowalczyk, P., Röder, M., Dürr, A., & Thiesse, F. (2022) · Proceedings of the Annual Hawaii International Conference on System Sciences · doi:10.24251/hicss.2022.184 · Cited by 9
Li, Y., Kong, C., Zhao, G., & Zhao, Z. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11337-0 · Cited by 7
LogGenST: A Framework for Synthetic Log Generation Using LLMs for Smart-Troubleshooting
Partovian, S., Flammini, F., Bucaioni, A., & Thornadtsson, J. (2025) · Lecture Notes in Business Information Processing · doi:10.1007/978-3-031-84913-8_3 · Cited by 3
Dai, J., Zhang, Y., Shi, C., Liu, Y., Xiu, P., & Wang, Y. (2024) · The Journal of Physical Chemistry B · doi:10.1021/acs.jpcb.4c06729 · Cited by 3
Machine Generated Text: A Comprehensive Survey of Threat Models and Detection Methods
Crothers, E., Japkowicz, N., & Viktor, H. (2022) · arXiv · doi:10.48550/arxiv.2210.07321 · Cited by 196
Metadata
- Authors
- Yu, L., Zhang, W., Wang, J., & Yu, Y.
- Venue
- Proceedings of the AAAI Conference on Artificial Intelligence
- Published
- 2017-02-13
- Volume
- 31
- Issue
- 1
- ISSN
- 2159-5399 (ISSN-L)
- 2374-3468
- OpenAlex
- W2964268978
- Cited by
- 2,285
- References
- 8
- FWCI
- 87.61
- Type
- conference paper
- Language
- en
- Open access
- diamond
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
