  As NNs permeate various scientific and industrial domains, understanding the
universality and reusability of their representations becomes crucial. At their
core, these networks create intermediate neural representations, indicated as
latent spaces, of the input data and subsequently leverage them to perform
specific downstream tasks. This dissertation focuses on the universality and
reusability of neural representations. Do the latent representations crafted by
a NN remain exclusive to a particular trained instance, or can they generalize
across models, adapting to factors such as randomness during training, model
architecture, or even data domain? This adaptive quality introduces the notion
of Latent Communication -- a phenomenon that describes when representations can
be unified or reused across neural spaces. A salient observation from our
research is the emergence of similarities in latent representations, even when
these originate from distinct or seemingly unrelated NNs. By exploiting a
partial correspondence between the two data distributions that establishes a
semantic link, we found that these representations can either be projected into
a universal representation, coined as Relative Representation, or be directly
translated from one space to another. Latent Communication allows for a bridge
between independently trained NN, irrespective of their training regimen,
architecture, or the data modality they were trained on -- as long as the data
semantic content stays the same (e.g., images and their captions). This holds
true for both generation, classification and retrieval downstream tasks; in
supervised, weakly supervised, and unsupervised settings; and spans various
data modalities including images, text, audio, and graphs -- showcasing the
universality of the Latent Communication phenomenon. [...]
