# Deep Convolutional GANs for Car Image Generation

arXiv: https://arxiv.org/abs/2006.14380 · HTML: https://mlsanity.com/paper/2006.14380/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Jun 2020.

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

- [A Study of Gender Classification Techniques Based on Iris Images: A Deep Survey and Analysis](https://mlsanity.com/paper/2508.05246/) (2025)

## Abstract (arXiv)

In this paper, we investigate the application of deep convolutional GANs on car image generation. We improve upon the commonly used DCGAN architecture by implementing Wasserstein loss to decrease mode collapse and introducing dropout at the end of the discrimiantor to introduce stochasticity. Furthermore, we introduce convolutional layers at the end of the generator to improve expressiveness and smooth noise. All of these improvements upon the DCGAN architecture comprise our proposal of the novel BoolGAN architecture, which is able to decrease the FID from 195.922 (baseline) to 165.966.