Data Augmentation via Variational Auto-Encoders
- 1.1k Downloads
Data augmentation is a widely considered technique to improve the performance of Convolutional Neural Networks during training. This step consists in synthetically generate new labeled data by perturbing the samples of the training set, which is expected to provide more robustness to the learning process. The problem is that the augmentation procedure has to be adjusted manually because the perturbations considered must make sense for the task at issue. In this paper we propose the use of Variational Auto-Encoders (VAEs) to generate new synthetic samples, instead of resorting to heuristic strategies. VAEs are powerful generative models that learn a parametric latent space of the input domain from which new samples can be generated. In our experiments over the well-known MNIST dataset, the data augmentation by VAEs improves the base results, yet to a lesser extent of that obtained by a well-adjusted conventional data augmentation. However, the combination of both conventional and VAE-guided data augmentations outperforms all the results, thereby demonstrating the goodness of our proposal.
KeywordsData augmentation Variational auto-encoders Convolutional Neural Networks MNIST dataset
This work was supported by the Spanish Ministerio de Ciencia, Innovación y Universidades through HISPAMUS project (Ref. TIN2017-86576-R, partially funded by UE FEDER funds).
- 3.Kingma, D.P., Welling, M.: Auto-encoding variational bayes. Computing Research Repository abs/1312.6114 (2013)Google Scholar
- 4.Krizhevsky, A., Sutskever, I., Hinton, G.E.: ImageNet classification with deep convolutional neural networks. In: 26th Annual Conference on Neural Information Processing Systems, pp. 1106–1114 (2012)Google Scholar
- 7.Rezende, D.J., Mohamed, S., Wierstra, D.: Stochastic backpropagation and approximate inference in deep generative models. In: 31th International Conference on Machine Learning, ICML 2014, Beijing, China, 21–26 June 2014, pp. 1278–1286 (2014)Google Scholar
- 8.Rumelhart, D.E., Hinton, G.E., Williams, R.J.: Learning internal representations by error propagation. In: Parallel Distributed Processing: Explorations in the Microstructure of Cognition, pp. 318–362. MIT Press, Cambridge (1986)Google Scholar