Abstract
In this paper, we examine whether we can use Generative Adversarial Networks as an oversampling technique for a largely imbalanced remote sensing dataset containing solar panels, endeavoring a better generalization ability on another geographical location. To this cause, we first analyze the image data by using several clustering methods on latent feature information extracted by a fine-tuned VGG16 network. After that, we use the cluster assignments as auxiliary input for training the GANs. In our experiments we have used three types of GANs: (1) conditional vanilla GANs, (2) conditional Wasserstein GANs, and (3) conditional Self-Attention GANs. The synthetic data generated by each of these GANs is evaluated by both the Fréchet Inception Distance and a comparison of a VGG11-based classification model with and without adding the generated positive images to the original source set. We show that all models are able to generate realistic outputs as well as improving the target performance. Furthermore, using the clusters as a GAN input showed to give a more diversified feature representation, improving stability of learning and lowering the risk of mode collapse.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Arjovsky, M., Bottou, L.: Towards principled methods for training generative adversarial networks. arXiv preprint arXiv:1701.04862 (2017)
Arjovsky, M., Chintala, S., Bottou, L.: Wasserstein gan. arXiv preprint arXiv:1701.07875 (2017)
Buda, M., Maki, A., Mazurowski, M.A.: A systematic study of the class imbalance problem in convolutional neural networks. Neural Netw. 106, 249–259 (2018)
Douzas, G., Bacao, F.: Effective data generation for imbalanced learning using conditional generative adversarial networks. Expert Syst. Appl. 91, 464–471 (2018)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Gulrajani, I., Ahmed, F., Arjovsky, M., Dumoulin, V., Courville, A.C.: Improved training of Wasserstein gans. In: Advances in Neural Information Processing Systems, pp. 5767–5777 (2017)
Lin, D., Fu, K., Wang, Y., Xu, G., Sun, X.: MARTA GANs: unsupervised representation learning for remote sensing image classification. IEEE Geosci. Remote Sens. Lett. 14(11), 2092–2096 (2017)
Lusa, L., et al.: Evaluation of smote for high-dimensional class-imbalanced microarray data. In: 11th International Conference on Machine Learning and Applications (2012)
Ma, D., Tang, P., Zhao, L.: SiftingGAN: generating and sifting labeled samples to improve the remote sensing image scene classification baseline in vitro. IEEE Geosci. Remote Sens. Lett. 16(7), 1046–1050 (2019)
Mariani, G., Scheidegger, F., Istrate, R., Bekas, C., Malossi, C.: BAGAN: data augmentation with balancing GAN. arXiv preprint arXiv:1803.09655 (2018)
Mullick, S.S., Datta, S., Das, S.: Generative adversarial minority oversampling. In: Proceedings of the IEEE/CVF International Conference on Computer Vision, pp. 1695–1704 (2019)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv:1511.06434 (2015)
Shamsolmoali, P., Zareapoor, M., Shen, L., Sadka, A.H., Yang, J.: Imbalanced data learning by minority class augmentation using capsule adversarial networks. Neurocomputing (2020)
Zhang, H., Goodfellow, I., Metaxas, D., Odena, A.: Self-attention generative adversarial networks. In: International Conference on Machine Learning, pp. 7354–7363. PMLR (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 Springer Nature Switzerland AG
About this paper
Cite this paper
Belderbos, I., de Jong, T., Popa, M. (2022). GANs Based Conditional Aerial Images Generation for Imbalanced Learning. In: El Yacoubi, M., Granger, E., Yuen, P.C., Pal, U., Vincent, N. (eds) Pattern Recognition and Artificial Intelligence. ICPRAI 2022. Lecture Notes in Computer Science, vol 13364. Springer, Cham. https://doi.org/10.1007/978-3-031-09282-4_28
Download citation
DOI: https://doi.org/10.1007/978-3-031-09282-4_28
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-09281-7
Online ISBN: 978-3-031-09282-4
eBook Packages: Computer ScienceComputer Science (R0)