Spatial Distribution Analysis of Images in GAN Training with Small Datasets

  • Lucas F. Buzuti Centro Universitário FEI
  • Tatiany M. Heiderich Centro Universitário FEI
  • Carlos E. Thomaz Centro Universitário FEI


Redes Adversárias Generativas (GANs) estão sendo cada vez mais usadas para gerar artificialmente vários tipos de dados. O treinamento dessas redes requer um conjunto de dados suficientemente grande e se torna um desafio com pequenos conjuntos. Trabalhos recentes propuseram novas abordagens para o treinamento de GANs com poucas amostras. Este trabalho analisa a distribuição espacial dos dados reais e sintéticos desses conjuntos, construindo subespaços de forma aleatória e variando o nível de espalhamento. Para variar o nível de espalhamento, este trabalho propõe o algoritmo k-Amostras Esparsas. Os resultados mostraram que pequenos conjuntos com uma distribuição espacial mais espalhada tendem a gerar dados com mais diversidade.


Goodfellow, I., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. (2014). Generative adversarial nets. Advances in neural information processing systems, 27.

Guan, S. and Loew, M. (2019). Evaluation of generative adversarial network performance based on direct analysis of generated images. In 2019 IEEE Applied Imagery Pattern Recognition Workshop (AIPR), pages 1-5. IEEE.

Heiderich, T. M., Leslie, A. T. F. S., and Guinsburg, R. (2015). Neonatal procedural pain can be assessed by computer software that has good sensitivity and specificity to detect facial movements. Acta Paediatrica, 104(2):e63-e69.

Heusel, M., Ramsauer, H., Unterthiner, T., Nessler, B., and Hochreiter, S. (2017). Gans trained by a two time-scale update rule converge to a local nash equilibrium. Advances in neural information processing systems, 30.

Karras, T., Aittala, M., Hellsten, J., Laine, S., Lehtinen, J., and Aila, T. (2020a). Training generative adversarial networks with limited data. In Proc. NeurIPS.

Karras, T., Laine, S., and Aila, T. (2019). A style-based generator architecture for generative adversarial networks. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 4396-4405.

Karras, T., Laine, S., Aittala, M., Hellsten, J., Lehtinen, J., and Aila, T. (2020b). Analyzing and improving the image quality of stylegan. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), pages 8107-8116.

Lin, Y., Li, L., Jing, H., Ran, B., and Sun, D. (2020). Automated traffic incident detection with a smaller dataset based on generative adversarial networks. Accident; analysis and prevention, 144:105628.

Miyato, T., Kataoka, T., Koyama, M., and Yoshida, Y. (2018). Spectral normalization for generative adversarial networks. arXiv preprint arXiv:1802.05957.

Noguchi, A. and Harada, T. (2019). Image generation from small datasets via batch statistics adaptation. 2019 IEEE/CVF International Conference on Computer Vision (ICCV), pages 2750-2758.

Norori, N., Hu, Q., Aellen, F. M., Faraci, F. D., and Tzovara, A. (2021). Addressing bias in big data and ai for health care: A call for open science. Patterns, 2(10):100347.

Salimans, T., Goodfellow, I., Zaremba, W., Cheung, V., Radford, A., and Chen, X. (2016). Improved techniques for training gans. Advances in neural information processing systems, 29:2234-2242.

Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J., and Wojna, Z. (2016). Rethinking the inception architecture for computer vision. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 2818-2826.

Thomaz, C. E. and Giraldi, G. A. (2010). A new ranking method for principal components analysis and its application to face image analysis. Image and vision computing, 28(6):902-913.

Wang, Y., Wu, C., Herranz, L., van de Weijer, J., Gonzalez-Garcia, A., and Raducanu, B. (2018). Transferring gans: generating images from limited data. In ECCV.

Wu, N., Liu, F., Meng, F., Li, M., Zhang, C., and He, Y. (2021). Rapid and accurate varieties classification of different crop seeds under sample-limited condition based on hyperspectral imaging and deep transfer learning. Frontiers in Bioengineering and Biotechnology, 9.

Yi, X., Walia, E., and Babyn, P. (2019). Generative adversarial network in medical imaging: A review. Medical image analysis, 58:101552.

Zhang, H., Sindagi, V., and Patel, V. M. (2019). Image de-raining using a conditional generative adversarial network. IEEE transactions on circuits and systems for video technology, 30(11):3943-3956.

Zhang, R., Isola, P., Efros, A. A., Shechtman, E., and Wang, O. (2018). The unreasonable effectiveness of deep features as a perceptual metric. In CVPR.
BUZUTI, Lucas F.; HEIDERICH, Tatiany M.; THOMAZ, Carlos E.. Spatial Distribution Analysis of Images in GAN Training with Small Datasets. In: ENCONTRO NACIONAL DE INTELIGÊNCIA ARTIFICIAL E COMPUTACIONAL (ENIAC), 19. , 2022, Campinas/SP. Anais [...]. Porto Alegre: Sociedade Brasileira de Computação, 2022 . p. 775-786. ISSN 2763-9061. DOI: