Review of methods and systems for generation of synthetic training data

Abstract


It is impossible to imagine the advancement of modern artificial intelligence systems without neural network technologies. During the design process researchers are often faced with the fact that there is not enough data to train modern neural network models, these data may be unbalanced or highly sparse. Often it happens that real data simply does not exist, as the research field is still emerging. A relevant problem is ensuring the confidentiality of real personal or patient medical data, which is used in the exchange between researchers or in the testing of various neural network systems. In many subject areas, the cost of collecting and marking up real data can be very high. Synthetic data is increasingly being used to solve these problems. The purpose of this publication is to introduce readers to advances in the generation and use of synthetic data. The paper presents a description of various methods, systems and software tools used to generate synthetic data, which can help to improve neural network models. Since an entire industry for synthetic data production has already formed, the leading data synthesis technology platforms are presented. The paper is of an overview nature, so it contains an extensive bibliography. The value of the article lies in the fact that this review will help readers broaden their understanding of the use of synthetic data in solving a wide range of neural network problems, as well as to become more familiar with the methods and tools for their generation.

Full Text

В процессе проектирования современных нейросетевых моделей исследователи часто сталкиваются с проблемой недоступности достаточного количества данных для их обучения, а также с неравномерностью или разреженностью этих данных. Нередко случается, что реальных данных просто не существует, так как область исследований еще только формируется. Также существует проблема конфиденциальности реальных персональных данных или медицинских данных пациентов, которые используются в процессе обмена между исследователями или в процессе тестирования различных нейросетевых систем. Во всех этих случаях на помощь приходят синтетические данные. Как следует из названия, синтетические данные, это данные, которые созданы искусственно, а не в результате реальных событий. Они часто создаются с помощью алгоритмов и используются для широкого спектра действий. Одно из первых упоминаний о применении синтетических данных встречается в связи с разработкой и тестированием системы обнаружения вторжений агентством DARPA 1 в 1998 и 1999 гг. [1]. Тестовые данные содержали сетевой трафик и файлы журнала системных вызовов из смоделированной большой компьютерной сети. Атакующие данные были сгенерированы синтетически на основе сценариев возможных атак, а фоновые данные – с помощью программных автоматов, имитирующих использование различных услуг. Использование синтетических данных позволило разработчикам смоделировать и протестировать различные сценарии вторжения, которые ранее еще не встречались. В тех случаях, когда новые службы еще только тестируются перед вводом в эксплуатацию, данные для обучения нейросетей могут просто отсутствовать. В этом случае для тестирования нужны синтезированные данные. Например, авторы статьи [2] уже в 2002 г. применяли синтетические данные при создании системы детектирования мошенничества.

About the authors

A. N. Rabchevsky

Perm State University; LLC “SEUSLAB”

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