# Variational Conditional GAN for Fine-grained Controllable Image Generation

arXiv: https://arxiv.org/abs/1909.09979 · HTML: https://mlsanity.com/paper/1909.09979/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 22 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [A Unified View of cGANs with and without Classifiers](https://mlsanity.com/paper/2111.01035/) (2021)

## Abstract (arXiv)

In this paper, we propose a novel variational generator framework for conditional GANs to catch semantic details for improving the generation quality and diversity. Traditional generators in conditional GANs simply concatenate the conditional vector with the noise as the input representation, which is directly employed for upsampling operations. However, the hidden condition information is not fully exploited, especially when the input is a class label. Therefore, we introduce a variational inference into the generator to infer the posterior of latent variable only from the conditional input, which helps achieve a variable augmented representation for image generation. Qualitative and quantitative experimental results show that the proposed method outperforms the state-of-the-art approaches and achieves the realistic controllable images.