The document describes how to generate and review images using machine learning. To generate images, a model learns from original images, searches topics to generate new images on, and iterates generating and scoring images until a threshold is passed. To review generated images, both input and generated images are disassembled into pixels, quantified by color, analyzed for tendencies, which are then compared between input and generated images to find the generated image most similar to the original tendencies.

1. Image generation
Hook
How to generate images?
Key details
1. Learn original images
2. Search about topics from learned
datas
3. Generate some images about topics
4. Identify generated images and give
a points to all each images
5. Return to 3 and loop until pass 3
How to review that generated data?
1. Get inputed images.
2. Disassemble images into nano pixels.
3. Quantify each nano pixels by its
color.
4. Analyse these images and find some
tendencies of these inputed images.
5. Qualify its tendencies. (=A)
6. Get generated images.
7. Analyse and quantify these image’s
tendencies the same process as 2~5.
(=B)
8. Compare A and B, and if B is nearest
from A in other generated images, This
is the image you target!!