# Maximum-Likelihood Augmented Discrete Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1702.07983 · HTML: https://mlsanity.com/paper/1702.07983/

## Key facts

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

## Built on by (most cited first, 30 of 39)

- [Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning](https://mlsanity.com/paper/2208.04202/) (2022)
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- [Target Concrete Score Matching: A Holistic Framework for Discrete Diffusion](https://mlsanity.com/paper/2504.16431/) (2025)
- [Diffusion-based Document Layout Generation](https://mlsanity.com/paper/2303.10787/) (2023)
- [A survey on text generation using generative adversarial networks](https://mlsanity.com/paper/2212.11119/) (2022)
- [TextBox: A Unified, Modularized, and Extensible Framework for Text Generation](https://mlsanity.com/paper/2101.02046/) (2021)
- [GAN Computers Generate Arts? A Survey on Visual Arts, Music, and Literary Text Generation using Generative Adversarial Network](https://mlsanity.com/paper/2108.03857/) (2021)
- [To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs](https://mlsanity.com/paper/2106.06363/) (2021)
- [Unaligned Image-to-Image Translation by Learning to Reweight](https://mlsanity.com/paper/2109.11736/) (2021)
- [Reinforcement Learning for Generative AI: A Survey](https://mlsanity.com/paper/2308.14328/) (2023)
- [Generative Cooperative Networks for Natural Language Generation](https://mlsanity.com/paper/2201.12320/) (2022)
- [Flexible Language Modeling in Continuous Space with Transformer-based Autoregressive Flows](https://mlsanity.com/paper/2507.00425/) (2025)
- [Improved Training of Mixture-of-Experts Language GANs](https://mlsanity.com/paper/2302.11875/) (2023)
- [PoetryDiffusion: Towards Joint Semantic and Metrical Manipulation in Poetry Generation](https://mlsanity.com/paper/2306.08456/) (2023)
- [Promises and Pitfalls of Generative Masked Language Modeling: Theoretical Framework and Practical Guidelines](https://mlsanity.com/paper/2407.21046/) (2024)
- [Soft-Di[M]O: Improving One-Step Discrete Image Generation with Soft Embeddings](https://mlsanity.com/paper/2509.22925/) (2025)
- [Generating Symbolic Reasoning Problems with Transformer GANs](https://mlsanity.com/paper/2110.10054/) (2021)
- [Segmentation-guided Domain Adaptation and Data Harmonization of Multi-device Retinal Optical Coherence Tomography using Cycle-Consistent Generative Adversarial Networks](https://mlsanity.com/paper/2208.14635/) (2022)
- [Adversarial Machine Learning in Text Analysis and Generation](https://mlsanity.com/paper/2101.08675/) (2021)
- [SparseGAN: Sparse Generative Adversarial Network for Text Generation](https://mlsanity.com/paper/2103.11578/) (2021)
- [A Novel Approach to Curiosity and Explainable Reinforcement Learning via Interpretable Sub-Goals](https://mlsanity.com/paper/2104.06630/) (2021)
- [Catch Me If You GAN: Using Artificial Intelligence for Fake Log Generation](https://mlsanity.com/paper/2112.12006/) (2021)
- [Survey of Generative Methods for Social Media Analysis](https://mlsanity.com/paper/2112.07041/) (2021)
- [FinGAN: Generative Adversarial Network for Analytical Customer Relationship Management in Banking and Insurance](https://mlsanity.com/paper/2201.11486/) (2022)
- [Why GANs are overkill for NLP](https://mlsanity.com/paper/2205.09838/) (2022)
- [InitialGAN: A Language GAN with Completely Random Initialization](https://mlsanity.com/paper/2208.02531/) (2022)
- [A Mutation-based Text Generation for Adversarial Machine Learning Applications](https://mlsanity.com/paper/2212.11808/) (2022)
- [3D GANs and Latent Space: A comprehensive survey](https://mlsanity.com/paper/2304.03932/) (2023)

## 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.