  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 is particularly relevant 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. Using this information is the key to deep internal-learning
strategies, which 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.
