# CycleGANAS: Differentiable Neural Architecture Search for CycleGAN

arXiv: https://arxiv.org/abs/2311.07162 · HTML: https://mlsanity.com/paper/2311.07162/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 30 of its 43 references that are in the map.
- First posted on arXiv 13 Nov 2023.

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

- [Image-to-Image Translation with Conditional Adversarial Networks](https://mlsanity.com/paper/1611.07004/) (2016)
- [DARTS: Differentiable Architecture Search](https://mlsanity.com/paper/1806.09055/) (2018)
- [Neural Architecture Search with Reinforcement Learning](https://mlsanity.com/paper/1611.01578/) (2016)
- [ESRGAN: Enhanced Super-Resolution Generative Adversarial Networks](https://mlsanity.com/paper/1809.00219/) (2018)
- [Efficient Neural Architecture Search via Parameter Sharing](https://mlsanity.com/paper/1802.03268/) (2018)
- [Which Training Methods for GANs do actually Converge?](https://mlsanity.com/paper/1801.04406/) (2018)
- [Generative Image Inpainting with Contextual Attention](https://mlsanity.com/paper/1801.07892/) (2018)
- [AutoML: A Survey of the State-of-the-Art](https://mlsanity.com/paper/1908.00709/) (2019)
- [SeqGAN: Sequence Generative Adversarial Nets with Policy Gradient](https://mlsanity.com/paper/1609.05473/) (2016)
- [Toward Multimodal Image-to-Image Translation](https://mlsanity.com/paper/1711.11586/) (2017)
- [Designing Neural Network Architectures using Reinforcement Learning](https://mlsanity.com/paper/1611.02167/) (2016)
- [Learning to Discover Cross-Domain Relations with Generative Adversarial Networks](https://mlsanity.com/paper/1703.05192/) (2017)
- [Neural Architecture Search with Bayesian Optimisation and Optimal Transport](https://mlsanity.com/paper/1802.07191/) (2018)
- [BANANAS: Bayesian Optimization with Neural Architectures for Neural Architecture Search](https://mlsanity.com/paper/1910.11858/) (2019)
- [MirrorGAN: Learning Text-to-image Generation by Redescription](https://mlsanity.com/paper/1903.05854/) (2019)
- [The Numerics of GANs](https://mlsanity.com/paper/1705.10461/) (2017)
- [Translating and Segmenting Multimodal Medical Volumes with Cycle- and Shape-Consistency Generative Adversarial Network](https://mlsanity.com/paper/1802.09655/) (2018)
- [AutoGAN: Neural Architecture Search for Generative Adversarial Networks](https://mlsanity.com/paper/1908.03835/) (2019)
- [Learning Graph Convolutional Network for Skeleton-based Human Action Recognition by Neural Searching](https://mlsanity.com/paper/1911.04131/) (2019)
- [BayesNAS: A Bayesian Approach for Neural Architecture Search](https://mlsanity.com/paper/1905.04919/) (2019)
- [CycleGAN-VC2: Improved CycleGAN-based Non-parallel Voice Conversion](https://mlsanity.com/paper/1904.04631/) (2019)
- [Geometry-Aware Gradient Algorithms for Neural Architecture Search](https://mlsanity.com/paper/2004.07802/) (2020)
- [AdversarialNAS: Adversarial Neural Architecture Search for GANs](https://mlsanity.com/paper/1912.02037/) (2019)
- [Co-Evolutionary Compression for Unpaired Image Translation](https://mlsanity.com/paper/1907.10804/) (2019)
- [Off-Policy Reinforcement Learning for Efficient and Effective GAN Architecture Search](https://mlsanity.com/paper/2007.09180/) (2020)
- [OPANAS: One-Shot Path Aggregation Network Architecture Search for Object Detection](https://mlsanity.com/paper/2103.04507/) (2021)
- [Evolving Space-Time Neural Architectures for Videos](https://mlsanity.com/paper/1811.10636/) (2018)
- [Asymmetric GAN for Unpaired Image-to-image Translation](https://mlsanity.com/paper/1912.11660/) (2019)
- [NAR-Former: Neural Architecture Representation Learning towards Holistic Attributes Prediction](https://mlsanity.com/paper/2211.08024/) (2022)
- [AGAN: Towards Automated Design of Generative Adversarial Networks](https://mlsanity.com/paper/1906.11080/) (2019)

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

- [The Dawn of KAN in Image-to-Image (I2I) Translation: Integrating Kolmogorov-Arnold Networks with GANs for Unpaired I2I Translation](https://mlsanity.com/paper/2408.08216/) (2024)

## Abstract (arXiv)

We develop a Neural Architecture Search (NAS) framework for CycleGAN that carries out unpaired image-to-image translation task. Extending previous NAS techniques for Generative Adversarial Networks (GANs) to CycleGAN is not straightforward due to the task difference and greater search space. We design architectures that consist of a stack of simple ResNet-based cells and develop a search method that effectively explore the large search space. We show that our framework, called CycleGANAS, not only effectively discovers high-performance architectures that either match or surpass the performance of the original CycleGAN, but also successfully address the data imbalance by individual architecture search for each translation direction. To our best knowledge, it is the first NAS result for CycleGAN and shed light on NAS for more complex structures.