Summary of Mechanisms Of Generative Image-to-image Translation Networks, by Guangzong Chen et al.
Mechanisms of Generative Image-to-Image Translation Networks
by Guangzong Chen, Mingui Sun, Zhi-Hong Mao, Kangni Liu, Wenyan Jia
First submitted to arxiv on: 15 Nov 2024
Categories
- Main: Computer Vision and Pattern Recognition (cs.CV)
- Secondary: Artificial Intelligence (cs.AI); Image and Video Processing (eess.IV)
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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 Generative Adversarial Networks (GANs) are a class of neural networks used in image-to-image translation. This paper proposes a simplified architecture for image-to-image translation, building on GANs and autoencoders. The authors investigate why using only the GAN component is effective for tasks like image translation. They show that adversarial training for GAN models yields comparable results to existing methods without added complexity. The paper explains the rationale behind this phenomenon and provides experimental results demonstrating its validity. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Generative Adversarial Networks (GANs) are a type of artificial intelligence used to change one picture into another. This research makes a simpler way to do this, using an idea called autoencoders. It shows why just using the GAN part is good enough for changing pictures and gets similar results as more complicated methods without extra work. The paper explains why this works and provides examples to prove it. |
Keywords
» Artificial intelligence » Gan » Translation
