Uma análise empírica do efeito do Ruído de Classe no aprendizado de Redes Neurais Artificiais
Resumo
O ruído de classe consiste no erro de rotulação da classe. Ele pode afetar negativamente o desempenho de um modelo, podendo variar com relação ao modelo escolhido. Por essa razão, surgiram trabalhos que avaliam a resistência natural dos modelos de Aprendizado de Máquina ao ruído de classe. Sendo assim, seria relevante avaliar a resistência natural da rede neural artificial ao ruído de classe, dado a sua relevância ao Aprendizado Profundo. O objetivo deste trabalho é realizar um experimento para avaliar a influência do ruído de classe nas redes neurais artificiais, treinando-as sob base de dados ruidosas. Os resultados mostraram que a complexidade da rede pode influenciar a resistência delas ao ruído de classe.
Palavras-chave:
Ruído de Classe, Redes Neurais Artificiais, Aprendizado de Máquina
Referências
Algan, G. and Ulusoy, I. (2020). Label noise types and their effects on deep learning. arXiv preprint arXiv:2003.10471.
Frénay, B. and Verleysen, M. (2013). Classification in the presence of label noise: a survey. IEEE transactions on neural networks and learning systems, 25(5):845-869.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/.
Han, B., Yao, Q., Liu, T., Niu, G., Tsang, I. W., Kwok, J. T., and Sugiyama, M. (2020). A survey of label-noise representation learning: Past, present and future. arXiv preprint arXiv:2011.04406.
LeCun, Y. (1998). The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist/.
Nettleton, D. F., Orriols-Puig, A., and Fornells, A. (2010). A study of the effect of different types of noise on the precision of supervised learning techniques. Artificial intelligence review, 33(4):275-306.
Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., and Qu, L. (2017). Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1944-1952.
Rolnick, D., Veit, A., Belongie, S., and Shavit, N. (2017). Deep learning is robust to massive label noise. arXiv preprint arXiv:1705.10694.
Rusiecki, A. (2020). Standard dropout as remedy for training deep neural networks with label noise. In International Conference on Dependability and Complex Systems, pages 534-542. Springer.
Russell, S. J. and Norvig, P. (2021). Artificial Intelligence: A Modern Approach. Pearson, global edition.
Simard, P. Y., Steinkraus, D., Platt, J. C., et al. (2003). Best practices for convolutional neural networks applied to visual document analysis. In Icdar, volume 3.
Song, H., Kim, M., Park, D., Shin, Y., and Lee, J.-G. (2020). Learning from noisy labels with deep neural networks: A survey. arXiv preprint arXiv:2007.08199.
Xiao, H., Rasul, K., and Vollgraf, R. (2017). Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747.
Zhang, Z. and Sabuncu, M. (2018). Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems, 31.
Frénay, B. and Verleysen, M. (2013). Classification in the presence of label noise: a survey. IEEE transactions on neural networks and learning systems, 25(5):845-869.
Goodfellow, I., Bengio, Y., and Courville, A. (2016). Deep Learning. MIT Press. https://www.deeplearningbook.org/.
Han, B., Yao, Q., Liu, T., Niu, G., Tsang, I. W., Kwok, J. T., and Sugiyama, M. (2020). A survey of label-noise representation learning: Past, present and future. arXiv preprint arXiv:2011.04406.
LeCun, Y. (1998). The mnist database of handwritten digits. http://yann.lecun.com/exdb/mnist/.
Nettleton, D. F., Orriols-Puig, A., and Fornells, A. (2010). A study of the effect of different types of noise on the precision of supervised learning techniques. Artificial intelligence review, 33(4):275-306.
Patrini, G., Rozza, A., Krishna Menon, A., Nock, R., and Qu, L. (2017). Making deep neural networks robust to label noise: A loss correction approach. In Proceedings of the IEEE conference on computer vision and pattern recognition, pages 1944-1952.
Rolnick, D., Veit, A., Belongie, S., and Shavit, N. (2017). Deep learning is robust to massive label noise. arXiv preprint arXiv:1705.10694.
Rusiecki, A. (2020). Standard dropout as remedy for training deep neural networks with label noise. In International Conference on Dependability and Complex Systems, pages 534-542. Springer.
Russell, S. J. and Norvig, P. (2021). Artificial Intelligence: A Modern Approach. Pearson, global edition.
Simard, P. Y., Steinkraus, D., Platt, J. C., et al. (2003). Best practices for convolutional neural networks applied to visual document analysis. In Icdar, volume 3.
Song, H., Kim, M., Park, D., Shin, Y., and Lee, J.-G. (2020). Learning from noisy labels with deep neural networks: A survey. arXiv preprint arXiv:2007.08199.
Xiao, H., Rasul, K., and Vollgraf, R. (2017). Fashion-mnist: a novel image dataset for benchmarking machine learning algorithms. arXiv preprint arXiv:1708.07747.
Zhang, Z. and Sabuncu, M. (2018). Generalized cross entropy loss for training deep neural networks with noisy labels. Advances in neural information processing systems, 31.
Publicado
19/09/2022
Como Citar
DE AZEVEDO, Lívia; BRAIDA, Filipe.
Uma análise empírica do efeito do Ruído de Classe no aprendizado de Redes Neurais Artificiais. In: WORKSHOP DE TRABALHOS DE ALUNOS DA GRADUAÇÃO (WTAG) - SIMPÓSIO BRASILEIRO DE BANCO DE DADOS (SBBD), 37. , 2022, Búzios.
Anais [...].
Porto Alegre: Sociedade Brasileira de Computação,
2022
.
p. 14-19.
DOI: https://doi.org/10.5753/sbbd_estendido.2022.21837.
