# cGANs with Conditional Convolution Layer

arXiv: https://arxiv.org/abs/1906.00709 · HTML: https://mlsanity.com/paper/1906.00709/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Jun 2019.

## Built on by (most cited first, 6 of 6)

- [Conditional GANs with Auxiliary Discriminative Classifier](https://mlsanity.com/paper/2107.10060/) (2021)
- [Generative Convolution Layer for Image Generation](https://mlsanity.com/paper/2111.15171/) (2021)
- [A Novel Generator with Auxiliary Branch for Improving GAN Performance](https://mlsanity.com/paper/2112.14968/) (2021)
- [Image Generation with Self Pixel-wise Normalization](https://mlsanity.com/paper/2201.10725/) (2022)
- [RelationRS: Relationship Representation Network for Object Detection in Aerial Images](https://mlsanity.com/paper/2110.06730/) (2021)
- [Effective Shortcut Technique for GAN](https://mlsanity.com/paper/2201.11351/) (2022)

## Abstract (arXiv)

Conditional generative adversarial networks (cGANs) have been widely researched to generate class conditional images using a single generator. However, in the conventional cGANs techniques, it is still challenging for the generator to learn condition-specific features, since a standard convolutional layer with the same weights is used regardless of the condition. In this paper, we propose a novel convolution layer, called the conditional convolution layer, which directly generates different feature maps by employing the weights which are adjusted depending on the conditions. More specifically, in each conditional convolution layer, the weights are conditioned in a simple but effective way through filter-wise scaling and channel-wise shifting operations. In contrast to the conventional methods, the proposed method with a single generator can effectively handle condition-specific characteristics. The experimental results on CIFAR, LSUN and ImageNet datasets show that the generator with the proposed conditional convolution layer achieves a higher quality of conditional image generation than that with the standard convolution layer.