  In this research work we present CLIP-GLaSS, a novel zero-shot framework to
generate an image (or a caption) corresponding to a given caption (or image).
CLIP-GLaSS is based on the CLIP neural network, which, given an image and a
descriptive caption, provides similar embeddings. Differently, CLIP-GLaSS takes
a caption (or an image) as an input, and generates the image (or the caption)
whose CLIP embedding is the most similar to the input one. This optimal image
(or caption) is produced via a generative network, after an exploration by a
genetic algorithm. Promising results are shown, based on the experimentation of
the image Generators BigGAN and StyleGAN2, and of the text Generator GPT2
