  Deep learning in general focuses on training a neural network from large
labeled datasets. Yet, in many cases there is value in training a network just
from the input at hand. This may involve training a network from scratch using
a single input or adapting an already trained network to a provided input
example at inference time. This survey paper aims at covering deep
internal-learning techniques that have been proposed in the past few years for
these two important directions. While our main focus will be on image
processing problems, most of the approaches that we survey are derived for
general signals (vectors with recurring patterns that can be distinguished from
noise) and are therefore applicable to other modalities. We believe that the
topic of internal-learning is very important in many signal and image
processing problems where training data is scarce and diversity is large on the
one hand, and on the other, there is a lot of structure in the data that can be
exploited.

Machine Learning;Computer Vision and Pattern Recognition;Image and Video Processing;Signal Processing