Abstract
Generative Adversarial Networks (GANs) are a machine learning approach that have the ability to generate novel images. Recent developments in deep learning have enabled a generation of compelling images using generative networks that encode images with lower-dimensional latent spaces. Nature-inspired optimisation methods has been used to generate new images. In this paper, we train GAN with aim of generating images that are created based on optimisation of feature scores in one or two dimensions. We use search in the latent space to generate images scoring high or low values feature measures and compare different feature measures. Our approach successfully generate image variations with two datasets, faces and butterflies. The work gives insights on how feature measures promote diversity of images and how the different measures interact.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
Note that for feature GCF maximisation is achieved through 1/GCF and scaling in the range [0, 1].
References
PyTorch tutorial: Dcgan. https://github.com/yunjey/pytorch-tutorial
McCormack, J., d’Inverno, M. (eds.): Computers and Creativity. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-31727-9
Alexander, B., Kortman, J., Neumann, A.: Evolution of artistic image variants through feature based diversity optimisation. In: GECCO, pp. 171–178 (2017)
Correia, J., Machado, P., Romero, J., Carballal, A.: Evolving figurative images using expression-based evolutionary art. In: ICCC, p. 24 (2013)
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: a large-scale hierarchical image database. In: CVPR, pp. 248–255. IEEE Computer Society (2009)
Dosovitskiy, A., Brox, T.: Generating images with perceptual similarity metrics based on deep networks. In: NIPS, pp. 658–666 (2016)
Dosovitskiy, A., Springenberg, J.T., Brox, T.: Learning to generate chairs with convolutional neural networks. In: CVPR, pp. 1538–1546. IEEE Computer Society (2015)
Gao, W., Nallaperuma, S., Neumann, F.: Feature-based diversity optimization for problem instance classification. In: Handl, J., Hart, E., Lewis, P.R., López-Ibáñez, M., Ochoa, G., Paechter, B. (eds.) PPSN 2016. LNCS, vol. 9921, pp. 869–879. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-45823-6_81
Gatys, L.A., Ecker, A.S., Bethge, M.: Texture synthesis using convolutional neural networks. In: NIPS, pp. 262–270 (2015)
Gatys, L.A., Ecker, A.S., Bethge, M.: Image style transfer using convolutional neural networks. In: CVPR, pp. 2414–2423. IEEE Computer Society (2016)
Gatys, L.A., Ecker, A.S., Bethge, M., Hertzmann, A., Shechtman, E.: Controlling perceptual factors in neural style transfer. In: CVPR, pp. 3730–3738. IEEE Computer Society (2017)
Goodfellow, I., Bengio, Y., Courville, A.: Deep Learning. MIT Press, Cambridge (2016)
Goodfellow, I., et al.: Generative adversarial nets. In: Advances in Neural Information Processing Systems, pp. 2672–2680 (2014)
Hansen, N., Müller, S.D., Koumoutsakos, P.: Reducing the time complexity of the derandomized evolution strategy with covariance matrix adaptation (CMA-ES). Evol. Comput. 11(1), 1–18 (2003)
den Heijer, E., Eiben, A.E.: Investigating aesthetic measures for unsupervised evolutionary art. Swarm Evol. Comput. 16, 52–68 (2014)
Kowaliw, T., Dorin, A., McCormack, J.: Promoting creative design in interactive evolutionary computation. IEEE Trans. Evol. Comput. 16(4), 523–536 (2012)
Liu, Z., Luo, P., Wang, X., Tang, X.: Deep learning face attributes in the wild. In: ICCV (2015)
Matkovic, K., Neumann, L., Neumann, A., Psik, T., Purgathofer, W.: Global contrast factor-a new approach to image contrast. In: Computational Aesthetics, pp. 159–168 (2005)
Nair, V., Hinton, G.E.: Rectified linear units improve restricted Boltzmann machines. In: ICML, pp. 807–814 (2010)
Neumann, A., Alexander, B., Neumann, F.: The evolutionary process of image transition in conjunction with box and strip mutation. In: Hirose, A., Ozawa, S., Doya, K., Ikeda, K., Lee, M., Liu, D. (eds.) ICONIP 2016. LNCS, vol. 9949, pp. 261–268. Springer, Cham (2016). https://doi.org/10.1007/978-3-319-46675-0_29
Neumann, A., Alexander, B., Neumann, F.: Evolutionary image transition using random walks. In: Correia, J., Ciesielski, V., Liapis, A. (eds.) EvoMUSART 2017. LNCS, vol. 10198, pp. 230–245. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-55750-2_16
Neumann, A., Neumann, F.: Evolutionary computation for digital art. In: GECCO, pp. 937–955. ACM (2018)
Neumann, A., Szpak, Z.L., Chojnacki, W., Neumann, F.: Evolutionary image composition using feature covariance matrices. In: GECCO, pp. 817–824 (2017)
Nguyen, A., Dosovitskiy, A., Yosinski, J., Brox, T., Clune, J.: Synthesizing the preferred inputs for neurons in neural networks via deep generator networks. In: NIPS, pp. 3387–3395 (2016)
Nixon, M., Aguado, A.S.: Feature Extraction & Image Processing, 2 edn. Academic Press, Boston (2008)
Radford, A., Metz, L., Chintala, S.: Unsupervised representation learning with deep convolutional generative adversarial networks. arXiv preprint arXiv:1511.06434 (2015)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2018 Springer Nature Switzerland AG
About this paper
Cite this paper
Neumann, A., Pyromallis, C., Alexander, B. (2018). Evolution of Images with Diversity and Constraints Using a Generative Adversarial Network. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_39
Download citation
DOI: https://doi.org/10.1007/978-3-030-04224-0_39
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-030-04223-3
Online ISBN: 978-3-030-04224-0
eBook Packages: Computer ScienceComputer Science (R0)