Mixing Synthetic and Real-world Datasets Strategy for Improved Generalization of the CNN

Młodzikowski, KamilBelter, Dominik2023-09-252023-09-252023Młodzikowski K., Belter D., Mixing Synthetic and Real-world Datasets Strategy for Improved Generalization of the CNN. W: Progress in Polish Artificial Intelligence Research 4, Wojciechowski A. (Ed.), Lipiński P. (Ed.)., Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, s. 435-441, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.68.978-83-66741-92-8http://hdl.handle.net/11652/4844https://doi.org/10.34658/9788366741928.68In 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.enDla wszystkich w zakresie dozwolonego użytkuFair use conditiondeep learningrobot perceptionarticulated objectsgłębokie uczenie siępercepcja robotówobiekty przeguboweMixing Synthetic and Real-world Datasets Strategy for Improved Generalization of the CNNRozdział - monografiaLicencja PŁLUT License10.34658/9788366741928.68