Enhancing Neural Network Performance with Generative Adversarial Networks
DOI:
https://doi.org/10.21070/pels.v10i1.3128Keywords:
Generative Adversarial Networks, Neural Networks, Data Augmentation, Synthetic Data, Adversarial RobustnessAbstract
General Background Deep neural networks achieve remarkable success in computer vision but remain susceptible to overfitting and data scarcity. Specific Background Generative Adversarial Networks offer a framework to produce high-fidelity synthetic images through competitive zero-sum training between generator and discriminator sub-networks. Knowledge Gap However, the direct impact of generative augmentation on network generalization and adversarial robustness under noisy conditions requires rigorous empirical quantification. Aims This study evaluates how integrating generative synthetic samples into the training pipeline enhances neural network classification accuracy and resilience. Results Incorporating augmented images into CIFAR-10 training improved classification accuracy from 89.7% to 94.3%, while simultaneously increasing resilience against FGSM, PGD, and CW adversarial attacks. Novelty A unified evaluation architecture combining Wasserstein gradient penalty formulations with noisy data testing is established. Implications These findings demonstrate that generative synthetic data effectively mitigates data limitations and stabilizes deep learning deployment in unpredictable operational environments.
Key Findings Highlights
Synthetic data augmentation increases convolutional neural network accuracy on CIFAR-10 to 94.3%.
Adversarial training enhances model resilience against FGSM, PGD, and CW attacks.
Generative samples mitigate overfitting and restore classification accuracy under noisy data conditions.
Keywords : Generative Adversarial Networks, Neural Networks, Data Augmentation, Synthetic Data, Adversarial Robustness
Downloads
References
Z. Huang, C. O. Dumitru, Z. Pan, B. Lei, and M. Datcu, "Classification of Large-Scale High-Resolution SAR Images With Deep Transfer Learning," IEEE Geoscience and Remote Sensing Letters, vol. 18, pp. 107–111, 2021.
L. Wang, X. Yang, H. Tan, X. Bai, and F. Zhou, "Few-Shot Class-Incremental SAR Target Recognition Based on Hierarchical Embedding and Incremental Evolutionary Network," IEEE Transactions on Geoscience and Remote Sensing, vol. 61, pp. 1–11, 2023.
J. Qin, Z. Liu, L. Ran, R. Xie, J. Tang, and Z. Guo, "A Target SAR Image Expansion Method Based on Conditional Wasserstein Deep Convolutional GAN for Automatic Target Recognition," IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 15, pp. 7153–7170, 2022.
X. Li, Z. Du, Y. Huang, and Z. Tan, "A Deep Translation (GAN) Based Change Detection Network for Optical and SAR Remote Sensing Images," ISPRS Journal of Photogrammetry and Remote Sensing, vol. 179, pp. 14–34, 2021.
S. Saha, F. Bovolo, and L. Bruzzone, "Building Change Detection in VHR SAR Images via Unsupervised Deep Transcoding," IEEE Transactions on Geoscience and Remote Sensing, vol. 59, pp. 1917–1929, 2021.
M. Fuentes Reyes, S. Auer, N. Merkle, C. Henry, and M. Schmitt, "SAR-to-Optical Image Translation Based on Conditional Generative Adversarial Networks—Optimization, Opportunities and Limits," Remote Sensing, vol. 11, p. 2067, 2019.
Y. Zhao, T. Celik, N. Liu, and H.-C. Li, "A Comparative Analysis of GAN-Based Methods for SAR-to-Optical Image Translation," IEEE Geoscience and Remote Sensing Letters, vol. 19, pp. 1–5, 2022.
Z. Zuo and Y. Li, "A SAR-to-Optical Image Translation Method Based on PIX2PIX," in Proceedings of 2021 IEEE International Geoscience and Remote Sensing Symposium (IGARSS), Brussels, Belgium, 2021, pp. 3026–3029.
J. Gao, Q. Yuan, J. Li, H. Zhang, and X. Su, "Cloud Removal With Fusion of High Resolution Optical and SAR Images Using Generative Adversarial Networks," Remote Sensing, vol. 12, p. 191, 2020.
X. Yang, Z. Wang, J. Zhao, and D. Yang, "FG-GAN: A Fine-Grained Generative Adversarial Network for Unsupervised SAR-to-Optical Image Translation," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–11, 2022.
P. Ebel, M. Schmitt, and X. X. Zhu, "Cloud Removal in Unpaired Sentinel-2 Imagery Using Cycle-Consistent GAN and SAR-Optical Data Fusion," in Proceedings of IGARSS 2020 IEEE International Geoscience and Remote Sensing Symposium, Waikoloa, HI, USA, 2020, pp. 2065–2068.
G. Baier, A. Deschemps, M. Schmitt, and N. Yokoya, "Synthesizing Optical and SAR Imagery From Land Cover Maps and Auxiliary Raster Data," IEEE Transactions on Geoscience and Remote Sensing, vol. 60, pp. 1–12, 2021.
F. Gao, X. Xu, J. Yu, M. Shang, X. Li, and D. Tao, "Complementary, Heterogeneous and Adversarial Networks for Image-to-Image Translation," IEEE Transactions on Image Processing, vol. 30, pp. 3487–3498, 2021.
H. Tang, D. Xu, N. Sebe, Y. Wang, J. J. Corso, and Y. Yan, "Multi-Channel Attention Selection GAN With Cascaded Semantic Guidance for Cross-View Image Translation," in Proceedings of IEEE/CVF Conference on Computer Vision and Pattern Recognition, Long Beach, CA, USA, 2019, pp. 2417–2426.
L. Jiang, C. Zhang, M. Huang, C. Liu, J. Shi, and C. C. Loy, "TSIT: A Simple and Versatile Framework for Image-to-Image Translation," in Proceedings of European Conference on Computer Vision, Glasgow, UK, 2020, pp. 206–222.
P. Esser, R. Rombach, A. Blattmann, and B. Ommer, "ImageBART: Bidirectional Context With Multinomial Diffusion for Autoregressive Image Synthesis," Advances in Neural Information Processing Systems, vol. 34, pp. 3518–3532, 2021.
J. Ho, C. Saharia, W. Chan, D. J. Fleet, M. Norouzi, and T. Salimans, "Cascaded Diffusion Models for High Fidelity Image Generation," Journal of Machine Learning Research, vol. 23, pp. 1–33, 2022.
T. Wang, L. Wu, and C. Sun, "A Coarse-to-Fine Approach for Dynamic-to-Static Image Translation," Pattern Recognition, vol. 123, p. 108373, 2022.
E. Agustsson, M. Tschannen, F. Mentzer, R. Timofte, and L. V. Gool, "Generative Adversarial Networks for Extreme Learned Image Compression," in Proceedings of IEEE/CVF International Conference on Computer Vision, Seoul, Republic of Korea, 2019, pp. 221–231.
C. Chen, D. Gong, H. Wang, Z. Li, and K.-Y. K. Wong, "Learning Spatial Attention for Face Super-Resolution," IEEE Transactions on Image Processing, vol. 30, pp. 1219–1231, 2020.
Downloads
Published
How to Cite
Issue
Section
Categories
License
Copyright (c) 2026 Mohammed Hamdan Yousif
This work is licensed under a Creative Commons Attribution 4.0 International License.
