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

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dc.contributor.authorMłodzikowski, Kamil
dc.contributor.authorBelter, Dominik
dc.date.accessioned2023-09-25T09:32:51Z
dc.date.available2023-09-25T09:32:51Z
dc.date.issued2023
dc.description.abstractIn 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.en_EN
dc.identifier.citationMłodzikowski 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.
dc.identifier.doi10.34658/9788366741928.68
dc.identifier.isbn978-83-66741-92-8
dc.identifier.urihttp://hdl.handle.net/11652/4844
dc.identifier.urihttps://doi.org/10.34658/9788366741928.68
dc.language.isoenen_EN
dc.page.numbers. 435-441
dc.publisherWydawnictwo Politechniki Łódzkiejpl_PL
dc.publisherLodz University of Technology Pressen_EN
dc.relation.ispartofWojciechowski A. (Ed.), Lipiński P. (Ed.)., Progress in Polish Artificial Intelligence Research 4, Seria: Monografie Politechniki Łódzkiej Nr. 2437, Wydawnictwo Politechniki Łódzkiej, Łódź 2023, ISBN 978-83-66741-92-8, doi: 10.34658/9788366741928.
dc.rightsDla wszystkich w zakresie dozwolonego użytkupl_PL
dc.rightsFair use conditionen_EN
dc.rights.licenseLicencja PŁpl_PL
dc.rights.licenseLUT Licenseen_EN
dc.subjectdeep learningen_EN
dc.subjectrobot perceptionen_EN
dc.subjectarticulated objectsen_EN
dc.subjectgłębokie uczenie siępl_PL
dc.subjectpercepcja robotówpl_PL
dc.subjectobiekty przegubowepl_PL
dc.titleMixing Synthetic and Real-world Datasets Strategy for Improved Generalization of the CNNen_EN
dc.typeRozdział - monografiapl_PL
dc.typeChapter - monographen_EN

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