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.

翻译：深度学习通常侧重于从大规模标注数据集中训练神经网络。然而，在许多情况下，仅从手头的输入训练网络具有重要价值。这可能涉及从头开始使用单一输入训练网络，或在推理阶段将已训练好的网络调整到给定的输入样本上。本文综述旨在涵盖过去几年中针对这两个重要方向提出的深度内部学习技术。虽然我们的主要关注点将是图像处理问题，但我们综述的大多数方法均针对通用信号（具有可区别于噪声的重复模式的向量）推导得出，因此也适用于其他模态。我们认为内部学习这一主题在许多信号和图像处理问题中至关重要——一方面训练数据稀缺且多样性较大，另一方面数据中存在大量可被利用的结构信息。