257 - Exploring GAN latent space to generate images with desired features​

Code generated in the video can be downloaded from here:
https://github.com/bnsreenu/python_fo...

UTKFace dataset (Used in this video): https://susanqq.github.io/UTKFace/

Haarcascade models, if interested in detecting faces and extracting them into new images.
https://github.com/opencv/opencv/tree...
Celeb Dataset (Not used in the video):
https://www.kaggle.com/jessicali9530/...

Description:
Latent space is hard to interpret unless conditioned using many classes.​
But, the latent space can be exploited using generated images.​
Here is how...

- Generate 10s of images using random latent vectors.​
- Identify many images within each category of interest (e.g., smiling man, neutral man, etc. )​
- Average the latent vectors for each category to get the mean representation in the latent space (for that category).​
- Use these mean latent vectors to generate images with features of interest. ​
In summary, you can find the latent vectors for Smiling Man, neutral face man, and a baby with a neutral face and then generate a smiling babyface by:

Smiling Man + Neutral Man - Neutral baby = Smiling Baby