Summary of Cyclegan with Better Cycles, by Tongzhou Wang et al.
CycleGAN with Better Cycles
by Tongzhou Wang, Yihan Lin
First submitted to arxiv on: 27 Aug 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Machine Learning (cs.LG)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The CycleGAN framework for image-to-image translation with unpaired datasets is enhanced by proposing three simple modifications to the cycle consistency loss [4]. These tweaks aim to reduce unrealistic images and artifacts in certain applications. By applying these modifications, the resulting models achieve better performance with fewer undesirable side effects. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper uses a special technique called CycleGAN to translate images from one category to another without any paired examples. While this works well in many cases, it can sometimes produce weird or unrealistic results. To fix this issue, the authors suggest three small changes to the way they train their models. These changes make the results look better and are less likely to contain strange artifacts. |
Keywords
» Artificial intelligence » Translation
