Deep learning has changed the way we work, compute and has made our lives a lot easier. As Andrej Karpathy mentioned it is indeed the software 2.0, as we have taught machines to figure things out…
The purpose of deep learning is to learn a representation of high dimensional and noisy data using a sequence of differentiable functions, i.e., geometric transformations, that can perhaps be used…
T. Karras, S. Laine, M. Aittala, J. Hellsten, J. Lehtinen, and T. Aila. 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition, CVPR 2020, Seattle, WA, USA, June 13-19, 2020, page 8107--8116. Computer Vision Foundation / IEEE, (2020)
W. Hung, Y. Tsai, Y. Liou, Y. Lin, and M. Yang. (2018)cite arxiv:1802.07934Comment: Accepted in BMVC 2018. Code and models available at https://github.com/hfslyc/AdvSemiSeg.
W. Hung, Y. Tsai, Y. Liou, Y. Lin, and M. Yang. (2018)cite arxiv:1802.07934Comment: Accepted in BMVC 2018. Code and models available at https://github.com/hfslyc/AdvSemiSeg.
S. Sankaranarayanan, Y. Balaji, C. Castillo, and R. Chellappa. (2017)cite arxiv:1704.01705Comment: Accepted as spotlight talk at CVPR 2018. Code available here: https://github.com/yogeshbalaji/Generate_To_Adapt.