# MixerGAN: An MLP-Based Architecture for Unpaired Image-to-Image Translation

arXiv: https://arxiv.org/abs/2105.14110 · HTML: https://mlsanity.com/paper/2105.14110/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 27 of its 30 references that are in the map.
- First posted on arXiv 28 May 2021.

## Builds on (most cited first, 27 of 27)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [PyTorch: An Imperative Style, High-Performance Deep Learning Library](https://mlsanity.com/paper/1912.01703/) (2019)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Rethinking the Inception Architecture for Computer Vision](https://mlsanity.com/paper/1512.00567/) (2015)
- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [Gaussian Error Linear Units (GELUs)](https://mlsanity.com/paper/1606.08415/) (2016)
- [Perceptual Losses for Real-Time Style Transfer and Super-Resolution](https://mlsanity.com/paper/1603.08155/) (2016)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Photo-Realistic Single Image Super-Resolution Using a Generative Adversarial Network](https://mlsanity.com/paper/1609.04802/) (2016)
- [MLP-Mixer: An all-MLP Architecture for Vision](https://mlsanity.com/paper/2105.01601/) (2021)
- [Demystifying MMD GANs](https://mlsanity.com/paper/1801.01401/) (2018)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Multimodal Unsupervised Image-to-Image Translation](https://mlsanity.com/paper/1804.04732/) (2018)
- [Contrastive Learning for Unpaired Image-to-Image Translation](https://mlsanity.com/paper/2007.15651/) (2020)
- [Unsupervised Image-to-Image Translation Networks](https://mlsanity.com/paper/1703.00848/) (2017)
- [Residual Attention Network for Image Classification](https://mlsanity.com/paper/1704.06904/) (2017)
- [DualGAN: Unsupervised Dual Learning for Image-to-Image Translation](https://mlsanity.com/paper/1704.02510/) (2017)
- [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https://mlsanity.com/paper/1703.05192/) (2017)
- [Coupled Generative Adversarial Networks](https://mlsanity.com/paper/1606.07536/) (2016)
- [Image to Image Translation for Domain Adaptation](https://mlsanity.com/paper/1712.00479/) (2017)
- [Deep Learning for Deepfakes Creation and Detection: A Survey](https://mlsanity.com/paper/1909.11573/) (2019)
- [BAGAN: Data Augmentation with Balancing GAN](https://mlsanity.com/paper/1803.09655/) (2018)
- [AttentionGAN: Unpaired Image-to-Image Translation using Attention-Guided Generative Adversarial Networks](https://mlsanity.com/paper/1911.11897/) (2019)
- [Unsupervised Attention-guided Image to Image Translation](https://mlsanity.com/paper/1806.02311/) (2018)
- [SPA-GAN: Spatial Attention GAN for Image-to-Image Translation](https://mlsanity.com/paper/1908.06616/) (2019)
- [Unsupervised Diverse Colorization via Generative Adversarial Networks](https://mlsanity.com/paper/1702.06674/) (2017)
- [SRPGAN: Perceptual Generative Adversarial Network for Single Image Super Resolution](https://mlsanity.com/paper/1712.05927/) (2017)

## Built on by (most cited first, 10 of 10)

- [Are we ready for a new paradigm shift? A Survey on Visual Deep MLP](https://mlsanity.com/paper/2111.04060/) (2021)
- [Skating-Mixer: Long-Term Sport Audio-Visual Modeling with MLPs](https://mlsanity.com/paper/2203.03990/) (2022)
- [Image-to-Image MLP-mixer for Image Reconstruction](https://mlsanity.com/paper/2202.02018/) (2022)
- [MLP-GAN for Brain Vessel Image Segmentation](https://mlsanity.com/paper/2207.08265/) (2022)
- [Guided Image-to-Image Translation by Discriminator-Generator Communication](https://mlsanity.com/paper/2303.03598/) (2023)
- [Increasing diversity of omni-directional images generated from single image using cGAN based on MLPMixer](https://mlsanity.com/paper/2309.08129/) (2023)
- [One-shot Detail Retouching with Patch Space Neural Transformation Blending](https://mlsanity.com/paper/2210.01217/) (2022)
- [MLP-SRGAN: A Single-Dimension Super Resolution GAN using MLP-Mixer](https://mlsanity.com/paper/2303.06298/) (2023)
- [Fully-fused Multi-Layer Perceptrons on Intel Data Center GPUs](https://mlsanity.com/paper/2403.17607/) (2024)
- [GAMA-IR: Global Additive Multidimensional Averaging for Fast Image Restoration](https://mlsanity.com/paper/2404.00807/) (2024)

## Abstract (arXiv)

While attention-based transformer networks achieve unparalleled success in nearly all language tasks, the large number of tokens (pixels) found in images coupled with the quadratic activation memory usage makes them prohibitive for problems in computer vision. As such, while language-to-language translation has been revolutionized by the transformer model, convolutional networks remain the de facto solution for image-to-image translation. The recently proposed MLP-Mixer architecture alleviates some of the computational issues associated with attention-based networks while still retaining the long-range connections that make transformer models desirable. Leveraging this memory-efficient alternative to self-attention, we propose a new exploratory model in unpaired image-to-image translation called MixerGAN: a simpler MLP-based architecture that considers long-distance relationships between pixels without the need for expensive attention mechanisms. Quantitative and qualitative analysis shows that MixerGAN achieves competitive results when compared to prior convolutional-based methods.