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.

翻译：深度学习的常规方法侧重于利用大规模标记数据集训练神经网络。然而，在许多情况下，仅凭手头输入训练网络具有重要价值。这在许多信号与图像处理问题中尤为突出——一方面训练数据匮乏且多样性极高，另一方面数据本身蕴含着大量可被利用的结构信息。利用这些信息正是深度内部学习策略的关键，这类策略可能包括从零开始使用单一输入训练网络，或在推理阶段根据给定输入示例对已训练网络进行适配。本综述旨在涵盖过去数年间针对上述两个重要方向提出的深度内部学习技术。虽然本文主要聚焦图像处理问题，但所综述的大多数方法均适用于通用信号（具有可区别于噪声的重复模式的向量），因此可推广至其他模态。