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 are 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 article 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 is 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.

中文翻译：

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深度内部学习：从单个输入进行深度学习


一般来说，深度学习侧重于从大型标记数据集训练神经网络。然而，在许多情况下，仅根据手头的输入来训练网络是有价值的。这在许多信号和图像处理问题中尤其相关，一方面，训练数据稀缺，多样性很大，另一方面，数据中有很多结构可以利用。使用这些信息是深度内部学习策略的关键，这可能涉及使用单个输入从头开始训练网络，或者在推理时将已经训练的网络调整为提供的输入示例。这篇调查文章旨在涵盖过去几年为这两个重要方向提出的深度内部学习技术。虽然我们主要关注图像处理问题，但我们调查的大多数方法都是针对一般信号（具有可以与噪声区分开来的具有重复模式的向量）得出的，因此适用于其他模态。