mlsanityData to 14 Oct 2026

Maximum-Likelihood Augmented Discrete Generative Adversarial Networks

arXiv 1702.07983 ↗First posted 26 Feb 2017cs.AI cs.CL cs.LGcited by 39

Not on the map. Why?

Abstract (arXiv)

Despite the successes in capturing continuous distributions, the application of generative adversarial networks (GANs) to discrete settings, like natural language tasks, is rather restricted. The fundamental reason is the difficulty of back-propagation through discrete random variables combined with the inherent instability of the GAN training objective. To address these problems, we propose Maximum-Likelihood Augmented Discrete Generative Adversarial Networks. Instead of directly optimizing the GAN objective, we derive a novel and low-variance objective using the discriminator's output that follows corresponds to the log-likelihood. Compared with the original, the new objective is proved to be consistent in theory and beneficial in practice. The experimental results on various discrete datasets demonstrate the effectiveness of the proposed approach.

Key facts. Cited by 39 papers in the map (data through 14 Oct 2026). First posted on arXiv 26 Feb 2017.

Builds on

No reference list yet.

Built on by

Cited by year (2026 to 14 Oct)

Citing papers per year: 2021: 13; 2022: 9; 2023: 7; 2024: 2; 2025: 5; 2026: 3.

39 papers.

Sort:

By area:

  1. 2022Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioningcited by 373
  2. 2021Step-unrolled Denoising Autoencoders for Text Generationcited by 102
  3. 2021Survey on reinforcement learning for language processingcited by 31
  4. 2024Inverse-RLignment: Large Language Model Alignment from Demonstrations through Inverse Reinforcement Learningcited by 27
  5. 2025Target Concrete Score Matching: A Holistic Framework for Discrete Diffusioncited by 25
  6. 2023Diffusion-based Document Layout Generationcited by 21
  7. 2022A survey on text generation using generative adversarial networkscited by 16
  8. 2021TextBox: A Unified, Modularized, and Extensible Framework for Text Generationcited by 15
  9. 2021GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Networkcited by 15
  10. 2021To Beam Or Not To Beam: That is a Question of Cooperation for Language GANscited by 14
  11. 2021Unaligned Image-to-Image Translation by Learning to Reweightcited by 13
  12. 2023Reinforcement Learning for Generative AI: A Surveycited by 11
27 more
  1. 2022Generative Cooperative Networks for Natural Language Generationcited by 8
  2. 2025Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flowscited by 8
  3. 2023Improved Training of Mixture-of-Experts Language GANscited by 7
  4. 2023PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generationcited by 7
  5. 2024Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelinescited by 6
  6. 2025Soft-Di[M]O: Improving One-Step Discrete Image Generation with Soft Embeddingscited by 6
  7. 2021Generating Symbolic Reasoning Problems with Transformer GANscited by 5
  8. 2022Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networkscited by 5
  9. 2021Adversarial Machine Learning in Text Analysis and Generationcited by 2
  10. 2021SparseGAN: Sparse Generative Adversarial Network for Text Generationcited by 2
  11. 2021A Novel Approach to Curiosity and Explainable Reinforcement Learning via Interpretable Sub-Goalscited by 2
  12. 2021Catch Me If You GAN: Using Artificial Intelligence for Fake Log Generationcited by 2
  13. 2021Survey of Generative Methods for Social Media Analysiscited by 1
  14. 2022FinGAN: Generative Adversarial Network for Analytical Customer Relationship Management in Banking and Insurancecited by 1
  15. 2022Why GANs are overkill for NLPcited by 1
  16. 2022InitialGAN: A Language GAN with Completely Random Initializationcited by 1
  17. 2022A Mutation-based Text Generation for Adversarial Machine Learning Applicationscited by 1
  18. 20233D GANs and Latent Space: A comprehensive surveycited by 1
  19. 2026ToxiGAN: Toxic Data Augmentation via LLM-Guided Directional Adversarial Generationcited by 1
  20. 2026Self-Generative Adversarial Fine-Tuning for Large Language Modelscited by 1
  21. 2021Using GAN-based models to sentimental analysis on imbalanced datasets in education domaincited by 0
  22. 2022Differential Privacy: What is all the noise about?cited by 0
  23. 2023Unlocking the Power of GANs in Non-Autoregressive Text Generationcited by 0
  24. 2023Unsupervised Text Embedding Space Generation Using Generative Adversarial Networks for Text Synthesiscited by 0
  25. 2025Detecting Spelling and Grammatical Anomalies in Russian Poetry Textscited by 0
  26. 2025Learning from Few Samples: A Novel Approach for High-Quality Malcode Generationcited by 0
  27. 2026Market Games for Generative Models: Equilibria, Welfare, and Strategic Entrycited by 0