# Modular Generative Adversarial Networks

arXiv: https://arxiv.org/abs/1804.03343 · HTML: https://mlsanity.com/paper/1804.03343/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 10 Apr 2018.

## Built on by (most cited first, 16 of 16)

- [Image-to-Image Translation: Methods and Applications](https://mlsanity.com/paper/2101.08629/) (2021)
- [Open World Compositional Zero-Shot Learning](https://mlsanity.com/paper/2101.12609/) (2021)
- [Image-to-image Translation via Hierarchical Style Disentanglement](https://mlsanity.com/paper/2103.01456/) (2021)
- [CAMPARI: Camera-Aware Decomposed Generative Neural Radiance Fields](https://mlsanity.com/paper/2103.17269/) (2021)
- [Compositional Transformers for Scene Generation](https://mlsanity.com/paper/2111.08960/) (2021)
- [3D-Aware Video Generation](https://mlsanity.com/paper/2206.14797/) (2022)
- [GAN-based Facial Attribute Manipulation](https://mlsanity.com/paper/2210.12683/) (2022)
- [Facial-Sketch Synthesis: A New Challenge](https://mlsanity.com/paper/2112.15439/) (2021)
- [Beyond RGB: Scene-Property Synthesis with Neural Radiance Fields](https://mlsanity.com/paper/2206.04669/) (2022)
- [Personalized Federated Learning via Heterogeneous Modular Networks](https://mlsanity.com/paper/2210.14830/) (2022)
- [ImageEye: Batch Image Processing Using Program Synthesis](https://mlsanity.com/paper/2304.03253/) (2023)
- [FBC-GAN: Diverse and Flexible Image Synthesis via Foreground-Background Composition](https://mlsanity.com/paper/2107.03166/) (2021)
- [CCR: Facial Image Editing with Continuity, Consistency and Reversibility](https://mlsanity.com/paper/2209.10734/) (2022)
- [Domain-Scalable Unpaired Image Translation via Latent Space Anchoring](https://mlsanity.com/paper/2306.14879/) (2023)
- [UTSGAN: Unseen Transition Suss GAN for Transition-Aware Image-to-image Translation](https://mlsanity.com/paper/2304.11955/) (2023)
- [Let's Split Up: Zero-Shot Classifier Edits for Fine-Grained Video Understanding](https://mlsanity.com/paper/2602.16545/) (2026)

## Abstract (arXiv)

Existing methods for multi-domain image-to-image translation (or generation) attempt to directly map an input image (or a random vector) to an image in one of the output domains. However, most existing methods have limited scalability and robustness, since they require building independent models for each pair of domains in question. This leads to two significant shortcomings: (1) the need to train exponential number of pairwise models, and (2) the inability to leverage data from other domains when training a particular pairwise mapping. Inspired by recent work on module networks, this paper proposes ModularGAN for multi-domain image generation and image-to-image translation. ModularGAN consists of several reusable and composable modules that carry on different functions (e.g., encoding, decoding, transformations). These modules can be trained simultaneously, leveraging data from all domains, and then combined to construct specific GAN networks at test time, according to the specific image translation task. This leads to ModularGAN's superior flexibility of generating (or translating to) an image in any desired domain. Experimental results demonstrate that our model not only presents compelling perceptual results but also outperforms state-of-the-art methods on multi-domain facial attribute transfer.