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Mixing Synthetic and Real-world Datasets Strategy for Improved Generalization of the CNN
(Wydawnictwo Politechniki Łódzkiej, 2023) Młodzikowski, Kamil; Belter, Dominik
In this paper, we deal with the problem of supervised training neural networks with an insufficient number of real-world training examples. We propose a method that at the beginning trains the neural network using a relatively simple synthetic dataset. In the following epochs, we add more challenging and real-life images to the training dataset. We compare the proposed strategy with other methods of using artificial and real-world datasets for training the neural network. The obtained results show that the proposed strategy allows for obtaining the neural network with higher generalization capabilities than competitive methods.