CycleGAN with Better Cycles
Not on the map. Why?
Abstract (arXiv)
CycleGAN provides a framework to train image-to-image translation with unpaired datasets using cycle consistency loss [4]. While results are great in many applications, the pixel level cycle consistency can potentially be problematic and causes unrealistic images in certain cases. In this project, we propose three simple modifications to cycle consistency, and show that such an approach achieves better results with fewer artifacts.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 3 of its 3 references that are in the map. First posted on arXiv 27 Aug 2024.
Builds on
3 of 3 references.
References · 3
- 2016Image-to-Image Translation with Conditional Adversarial Networkscited by 4,527
- 2016Least Squares Generative Adversarial Networkscited by 1,001
- 2016Generating Images with Perceptual Similarity Metrics based on Deep Networkscited by 222
Built on by
Citing papers per year: 2026: 2.
2 papers.