figure cont inuous space of faces generat ed by tom whit using vaes concept vectors for image editing we already hinted at the idea of concept vector when we covered word embeddings in the idea is still the samegiven latent space of representationsor an embedding spacecertain directions in the space may encode interesting axes of variation in the original data in latent space of images of facesfor instancethere may be smile vector ssuch that if latent point is the embedded representation of certain facethen latent point is the embedded representation of the same facesmiling once you've identified such vectorit then becomes possible to edit images by projecting them into the latent spacemoving their representation in meaningful wayand then decoding them back to image space there are concept vectors for essentially any independent dimension of variation in image space--in the case of facesyou may discover vectors for adding sunglasses to faceremoving glassesturning male face into as female faceand so on figure is an example of smile vectora concept vector discovered by tom white from the victoria university school of design in new zealandusing vaes trained on dataset of faces of celebrities (the celeba datasetlicensed to