It's all about image

Abstract

Image recognition technology is advancing rapidly. Researchers are discovering new ways to tackle the task without enormous datasets.

References

1. Nguyen, A., Yosinski, J., Bengio, Y., Dosovitskiy, A., and Clune, J. Plug & Play Generative Networks: Conditional Iterative Generation of Images in Latent Space. Computer Vision and Pattern Recognition (CVPR '17), 2017. http://www.evolvingai.org/ppgn.
2. Lanusse, F., Quanbin, M, Li, N., Collett, T.E., Li, C., Ravanbakhsh, S., Mandelbaun, R., and Poczos, B. CMU DeepLens: Deep Learning for Automatic Image-based Galaxy-Galaxy Strong Lens Finding. March 2017. arXiv:1703.02642. https://arxiv.org/abs/1703.02642.
3. Wang, K., Guo, P., Luo, A., Xin, X., and Duan, F. Deep neural networks with local connectivity and its application to astronomical spectral data. 2016 IEEE International Conference on Systems, Man, and Cybernetics (SMC), Budapest, 2016, pp. 002687-002692. http://ieeexplore.ieee.org/document/7844646/.
4. Goodfellow, I.J., Pouget-Abadie, J., Mirza, M., Xu, B., Warde-Farley, D., Ozair, S., Courville, A., and Bengio, Y. Generative Adversarial Networks. June 2014. eprint arXiv:1406.2661. http://adsabs.harvard.edu/cgi-bin/bib_query?arXiv:1406.2661.

Index Terms

• Computing methodologies

  • Artificial intelligence

    • Computer vision

      • Computer vision problems

        • Object recognition