Mixing Synthetic and Real-world Datasets Strategy for Improved Generalization of the CNN
Ładowanie...
Data
2023
Autorzy
Tytuł czasopisma
ISSN czasopisma
Tytuł tomu
Wydawca
Wydawnictwo Politechniki Łódzkiej
Lodz University of Technology Press
Lodz University of Technology Press
Abstrakt
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.
Opis
Słowa kluczowe
deep learning, robot perception, articulated objects, głębokie uczenie się, percepcja robotów, obiekty przegubowe
Cytowanie
Młodzikowski 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.