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The point is that the image has been transformed into a latent space encoding and interpolating those latent-space variables creates those mind-blowing effects
by bitL 8y ago
The point is that the image has been transformed into a latent space encoding and interpolating those latent-space variables creates those mind-blowing effects (imagine just moving from one value to another via a simple convex combination). How are those latent variables constructed and what do they represent (or if they could be described easily) is completely up to non-linear optimization process running on a huge number of dimensions.
- femto113 8y agoI don't understand this as the image being transformed, "space" and "dimensions" seem to originate not with the visual features of the images but with the attributes that the CelebA image set is annotated with. The coverage of the set is not uniform with respect to gender and skin color, I'm not 100% sure how to interpret the attribute values but a some quick math shows that images with "Pale_Skin" outnumber their opposites by about 20 to 1.