# Improving GANs with a Feature Cycling Generator

arXiv: https://arxiv.org/abs/2210.09638 · HTML: https://mlsanity.com/paper/2210.09638/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 36 of its 46 references that are in the map.
- First posted on arXiv 18 Oct 2022.

## Builds on (most cited first, 30 of 36)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [A Style-Based Generator Architecture for Generative Adversarial Networks](https://mlsanity.com/paper/1812.04948/) (2018)
- [Generative Adversarial Networks](https://mlsanity.com/paper/2203.00667/) (2022)
- [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)
- [Analyzing and Improving the Image Quality of StyleGAN](https://mlsanity.com/paper/1912.04958/) (2019)
- [Large Scale GAN Training for High Fidelity Natural Image Synthesis](https://mlsanity.com/paper/1809.11096/) (2018)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Spectral Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/1802.05957/) (2018)
- [LSUN: Construction of a Large-scale Image Dataset using Deep Learning with Humans in the Loop](https://mlsanity.com/paper/1506.03365/) (2015)
- [Alias-Free Generative Adversarial Networks](https://mlsanity.com/paper/2106.12423/) (2021)
- [StarGAN v2: Diverse Image Synthesis for Multiple Domains](https://mlsanity.com/paper/1912.01865/) (2019)
- [StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation](https://mlsanity.com/paper/1711.09020/) (2017)
- [Least Squares Generative Adversarial Networks](https://mlsanity.com/paper/1611.04076/) (2016)
- [Improved Precision and Recall Metric for Assessing Generative Models](https://mlsanity.com/paper/1904.06991/) (2019)
- [Generative Adversarial Text to Image Synthesis](https://mlsanity.com/paper/1605.05396/) (2016)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [Free-Form Image Inpainting with Gated Convolution](https://mlsanity.com/paper/1806.03589/) (2018)
- [Assessing Generative Models via Precision and Recall](https://mlsanity.com/paper/1806.00035/) (2018)
- [Improving the Adversarial Robustness and Interpretability of Deep Neural Networks by Regularizing their Input Gradients](https://mlsanity.com/paper/1711.09404/) (2017)
- [Consistency Regularization for Generative Adversarial Networks](https://mlsanity.com/paper/1910.12027/) (2019)
- [Stabilizing Training of Generative Adversarial Networks through Regularization](https://mlsanity.com/paper/1705.09367/) (2017)
- [Inferring Semantic Layout for Hierarchical Text-to-Image Synthesis](https://mlsanity.com/paper/1801.05091/) (2018)
- [Self-Supervised GANs via Auxiliary Rotation Loss](https://mlsanity.com/paper/1811.11212/) (2018)
- [Improved Consistency Regularization for GANs](https://mlsanity.com/paper/2002.04724/) (2020)
- [Improving the Improved Training of Wasserstein GANs: A Consistency Term and Its Dual Effect](https://mlsanity.com/paper/1803.01541/) (2018)
- [Gradient Normalization for Generative Adversarial Networks](https://mlsanity.com/paper/2109.02235/) (2021)
- [PEPSI++: Fast and Lightweight Network for Image Inpainting](https://mlsanity.com/paper/1905.09010/) (2019)
- [Why Spectral Normalization Stabilizes GANs: Analysis and Improvements](https://mlsanity.com/paper/2009.02773/) (2020)
- [Improving GANs with A Dynamic Discriminator](https://mlsanity.com/paper/2209.09897/) (2022)

## Abstract (arXiv)

Generative adversarial networks (GANs), built with a generator and discriminator, significantly have advanced image generation. Typically, existing papers build their generators by stacking up multiple residual blocks since it makes ease the training of generators. However, some recent papers commented on the limitation of the residual block and proposed a new architectural unit that improves the GANs performance. Following this trend, this paper presents a novel unit, called feature cycling block (FCB), which achieves impressive results in the image generation task. Specifically, the FCB has two branches: one is a memory branch and the other is an image branch. The memory branch keeps meaningful information at each stage of the generator, whereas the image branch takes some useful features from the memory branch to produce a high-quality image. To show the capability of the proposed method, we conducted extensive experiments using various datasets including CIFAR-10, CIFAR-100, FFHQ, AFHQ, and subsets of LSUN. Experimental results demonstrate the substantial superiority of our approach over the baseline without incurring any objective functions or training skills. For instance, the proposed method improves Frechet inception distance (FID) of StyleGAN2 from 4.89 to 3.72 on the FFHQ dataset and from 6.64 to 5.57 on the LSUN Bed dataset. We believe that the pioneering attempt presented in this paper could inspire the community with better-designed generator architecture and with training objectives or skills compatible with the proposed method.