Difference between revisions of "Generative Facial Prior-Generative Adversarial Network (GFP-GAN)"
(Created page with "{{#seo: |title=PRIMO.ai |titlemode=append |keywords=artificial, intelligence, machine, learning, models, algorithms, data, singularity, moonshot, Tensorflow, Google, Nvidia, M...") |
m |
||
| Line 17: | Line 17: | ||
Conventional methods fine-tune an existing AI model to restore images by gauging differences between the artificial and real photos. That frequently leads to low-quality results, the scientists said. The new approach uses a pre-trained version of an existing model (NVIDIA's StyleGAN-2) to inform the team's own model at multiple stages during the image generation process. The technique aims to preserve the "identity" of people in a photo, with a particular focus on facial features like eyes and mouths. | Conventional methods fine-tune an existing AI model to restore images by gauging differences between the artificial and real photos. That frequently leads to low-quality results, the scientists said. The new approach uses a pre-trained version of an existing model (NVIDIA's StyleGAN-2) to inform the team's own model at multiple stages during the image generation process. The technique aims to preserve the "identity" of people in a photo, with a particular focus on facial features like eyes and mouths. | ||
| + | |||
<youtube>nLDVtzcSeqM</youtube> | <youtube>nLDVtzcSeqM</youtube> | ||
| + | <youtube>d9ggCKl7sNQ</youtube> | ||
<youtube>h8kh-U2aRAc</youtube> | <youtube>h8kh-U2aRAc</youtube> | ||
| + | <youtube>DaxLjenHyrc</youtube> | ||
Revision as of 14:50, 31 July 2022
YouTube search... ...Google search
- Generative Adversarial Network (GAN)
- Semi-Supervised Learning with Generative Adversarial Network (SSL-GAN)
- Context-Conditional Generative Adversarial Network (CC-GAN)
- Image-to-Image Translation
- Autoencoder (AE) / Encoder-Decoder
- Variational Autoencoder (VAE)
- Generated Image
Conventional methods fine-tune an existing AI model to restore images by gauging differences between the artificial and real photos. That frequently leads to low-quality results, the scientists said. The new approach uses a pre-trained version of an existing model (NVIDIA's StyleGAN-2) to inform the team's own model at multiple stages during the image generation process. The technique aims to preserve the "identity" of people in a photo, with a particular focus on facial features like eyes and mouths.
