  This work addresses the problem of discovering non-linear interpretable paths
in the latent space of pre-trained GANs in a model-agnostic manner. In the
proposed method, the discovery is driven by a set of pairs of natural language
sentences with contrasting semantics, named semantic dipoles, that serve as the
limits of the interpretation that we require by the trainable latent paths to
encode. By using the pre-trained CLIP encoder, the sentences are projected into
the vision-language space, where they serve as dipoles, and where RBF-based
warping functions define a set of non-linear directional paths, one for each
semantic dipole, allowing in this way traversals from one semantic pole to the
other. By defining an objective that discovers paths in the latent space of
GANs that generate changes along the desired paths in the vision-language
embedding space, we provide an intuitive way of controlling the underlying
generative factors and address some of the limitations of the state-of-the-art
works, namely, that a) they are typically tailored to specific GAN
architectures (i.e., StyleGAN), b) they disregard the relative position of the
manipulated and the original image in the image embedding and the relative
position of the image and the text embeddings, and c) they lead to abrupt image
manipulations and quickly arrive at regions of low density and, thus, low image
quality, providing limited control of the generative factors. We provide
extensive qualitative and quantitative results that demonstrate our claims with
two pre-trained GANs, and make the code and the pre-trained models publicly
available at: https://github.com/chi0tzp/ContraCLIP
