# Conditional Adversarial Generative Flow for Controllable Image Synthesis

arXiv: https://arxiv.org/abs/1904.01782 · HTML: https://mlsanity.com/paper/1904.01782/

## Key facts

- Cited by 19 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Apr 2019.

## Built on by (most cited first, 19 of 19)

- [Low-Light Image Enhancement with Normalizing Flow](https://mlsanity.com/paper/2109.05923/) (2021)
- [DivCo: Diverse Conditional Image Synthesis via Contrastive Generative Adversarial Network](https://mlsanity.com/paper/2103.07893/) (2021)
- [Controllable Data Generation by Deep Learning: A Review](https://mlsanity.com/paper/2207.09542/) (2022)
- [DDPM-CD: Denoising Diffusion Probabilistic Models as Feature Extractors for Change Detection](https://mlsanity.com/paper/2206.11892/) (2022)
- [Conditional Synthetic Data Generation for Robust Machine Learning Applications with Limited Pandemic Data](https://mlsanity.com/paper/2109.06486/) (2021)
- [Target-aware Molecular Graph Generation](https://mlsanity.com/paper/2202.04829/) (2022)
- [Emerging Synergies in Causality and Deep Generative Models: A Survey](https://mlsanity.com/paper/2301.12351/) (2023)
- [Approaching the Limit of Image Rescaling via Flow Guidance](https://mlsanity.com/paper/2111.05133/) (2021)
- [PluGeN: Multi-Label Conditional Generation From Pre-Trained Models](https://mlsanity.com/paper/2109.09011/) (2021)
- [CDCGen: Cross-Domain Conditional Generation via Normalizing Flows and Adversarial Training](https://mlsanity.com/paper/2108.11368/) (2021)
- [Forward Operator Estimation in Generative Models with Kernel Transfer Operators](https://mlsanity.com/paper/2112.00305/) (2021)
- [Learned Bayesian Cram'er-Rao Bound for Unknown Measurement Models Using Score Neural Networks](https://mlsanity.com/paper/2502.00724/) (2025)
- [CEPAE: Conditional Entropy-Penalized Autoencoders for Time Series Counterfactuals](https://mlsanity.com/paper/2602.15546/) (2026)
- [Label Geometry Aware Discriminator for Conditional Generative Networks](https://mlsanity.com/paper/2105.05501/) (2021)
- [TRIP: Refining Image-to-Image Translation via Rival Preferences](https://mlsanity.com/paper/2111.13411/) (2021)
- [Maximum Likelihood on the Joint (Data, Condition) Distribution for Solving Ill-Posed Problems with Conditional Flow Models](https://mlsanity.com/paper/2208.11782/) (2022)
- [DTA: Distribution Transform-based Attack for Query-Limited Scenario](https://mlsanity.com/paper/2312.07245/) (2023)
- [StyleAutoEncoder for manipulating image attributes using pre-trained StyleGAN](https://mlsanity.com/paper/2412.20164/) (2024)
- [Decafs: Disentangled Conditional adversarial Flows](https://mlsanity.com/paper/2607.18755/) (2026)

## Abstract (arXiv)

Flow-based generative models show great potential in image synthesis due to its reversible pipeline and exact log-likelihood target, yet it suffers from weak ability for conditional image synthesis, especially for multi-label or unaware conditions. This is because the potential distribution of image conditions is hard to measure precisely from its latent variable z. In this paper, based on modeling a joint probabilistic density of an image and its conditions, we propose a novel flow-based generative model named conditional adversarial generative flow (CAGlow). Instead of disentangling attributes from latent space, we blaze a new trail for learning an encoder to estimate the mapping from condition space to latent space in an adversarial manner. Given a specific condition c, CAGlow can encode it to a sampled z, and then enable robust conditional image synthesis in complex situations like combining person identity with multiple attributes. The proposed CAGlow can be implemented in both supervised and unsupervised manners, thus can synthesize images with conditional information like categories, attributes, and even some unknown properties. Extensive experiments show that CAGlow ensures the independence of different conditions and outperforms regular Glow to a significant extent.