Regularization for Deep Learning: A Taxonomy
This work provides a clarifying overview for researchers and practitioners in deep learning, but it is incremental as it organizes existing knowledge rather than introducing new methods.
The paper tackles the problem of inconsistent definitions and isolated studies of regularization methods in deep learning by presenting a systematic taxonomy to categorize existing approaches, resulting in a unified framework that reveals links and similarities between methods.
Regularization is one of the crucial ingredients of deep learning, yet the term regularization has various definitions, and regularization methods are often studied separately from each other. In our work we present a systematic, unifying taxonomy to categorize existing methods. We distinguish methods that affect data, network architectures, error terms, regularization terms, and optimization procedures. We do not provide all details about the listed methods; instead, we present an overview of how the methods can be sorted into meaningful categories and sub-categories. This helps revealing links and fundamental similarities between them. Finally, we include practical recommendations both for users and for developers of new regularization methods.