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
258 - Semi-supervised learning with GANs
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247 - Conditional GANs and their applications
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