Abstract
An automatic Monet-style images generation method using long short term memory recurrent neural network is proposed in this paper. The method shows that long short term memory recurrent neural network can learn the structure and characteristics of Monet’s paintings properly by demonstrating its ability to generate impressionism-style images. With Monet’s paintings as input, similar style of images can be constructed using the proposed method iteratively. The experiment results indicate that the trained recurrent neural networks were able to generate Monet-style images with a small amount of training data.
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References
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9(8), 1735–1780 (1997)
Sutskever, I., Martens, J., Hinton, G.: Generating text with recurrent neural networks. In: Proceedings of ICML (2011)
Graves, A., Mohamed, A., Hinton, G.: Speech recognition with deep recurrent neural networks. In: Proceedings of ICASSP (2013)
Graves, A., Schmidhuber, J.: Offline handwriting recognition with multidimensional recurrent neural networks. In: Advances in Neural Information Processing Systems, vol. 21 (2008)
Kyprianidis, J.E., Collomosse, J., Wang, T., Isenberg, T.: State of the art: a taxonomy of artistic stylization techniques for images and video. IEEE Trans. Vis. Comput. Graph. 19(5), 866–885 (2013)
Hertzmann, A., Jacobs, C.E., Oliver, N., Curless, B., Salesin, D.H.: Image analogies. In: Proceedings of the 28th Annual Conference on Computer Graphics and Interactive Techniques, pp. 327–340 (2001)
Ashikhmin, N.: Fast texture transfer. IEEE Comput. Graph. Appl. 23, 38–43 (2003)
Mordvintsev, A., Olah, C., Tyka, M.: DeepDream - a code example for visualizing neural networks. Google Res. (2015)
Karpathy, A., Fei-Fei, L.: Deep visual-semantic alignments for generating image description. In: CVPR 2015 (2015)
Angeliki, L., Dat, T.N., Bernardi, R., Baroni, M.: Unveiling the dreams of word embeddings: towards language-driven image generation. arXiv:1506.03500
Gatys, L.A., Alexander, S.E., Matthias, B.: A neural algorithm of artistic style. arXiv:1508.06576
Bastien, F., Lamblin, P., Pascanu, R., Bergstra, J., Goodfellow, I., Bergeron, A., Bouchard, N., Bengio, Y.: Theano: new features and speed improvements. In: NIPS Workshop on Deep Learning and Unsupervised Feature Learning (2012)
Bergstra, J., Breuleux, O., Bastien, F., Lamblin, P., Pascanu, R., Desjardins, G., Turian, J., Warde-Farley, D., Bengio, Y.: Theano: a CPU and GPU math expression compiler. In: Proceedings of the Python for Scientific Computing Conference (SciPy), June 2010
Acknowledgments
This research was funded by the grants (No. 61540062) from the Natural Science Foundation of China.
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Zhao, Y., Xu, D. (2016). Monet-Style Images Generation Using Recurrent Neural Networks. In: El Rhalibi, A., Tian, F., Pan, Z., Liu, B. (eds) E-Learning and Games. Edutainment 2016. Lecture Notes in Computer Science(), vol 9654. Springer, Cham. https://doi.org/10.1007/978-3-319-40259-8_18
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DOI: https://doi.org/10.1007/978-3-319-40259-8_18
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