When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations-Reference-Cited by-同舟云学术

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When and how convolutional neural networks generalize to out-of-distribution category–viewpoint combinations

Published:2022-02-21 Issue:2 Volume:4 Page:146-153
ISSN:2522-5839
Container-title:Nature Machine Intelligence
language:en
Short-container-title:Nat Mach Intell

Author:

Madan Spandan^ORCID,Henry Timothy,Dozier Jamell,Ho Helen,Bhandari Nishchal,Sasaki Tomotake^ORCID,Durand Frédo,Pfister Hanspeter,Boix Xavier^ORCID

Funder

National Science Foundation

Fujitsu

Google Faculty Research Award

MIT-Sensetime Alliance on Artificial Intelligence

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Computer Networks and Communications,Computer Vision and Pattern Recognition,Human-Computer Interaction,Software

Link

https://www.nature.com/articles/s42256-021-00437-5.pdf

Reference68 articles.

1. He, K., Zhang, X., Ren, S. and Sun, J. Deep residual learning for image recognition. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 770–778 (IEEE, 2016).

2. Szegedy, C., Vanhoucke, V., Ioffe, S., Shlens, J. & Wojna, Z. Rethinking the inception architecture for computer vision. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 2818–2826 (IEEE, 2016).

3. Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q. Densely connected convolutional networks. In Proc. IEEE/CVF Conference on Computer Vision and Pattern Recognition 4700–4708 (IEEE, 2017).

4. Su, H., Qi, C. R., Li, Y. & Guibas, L. J. Render for CNN: Viewpoint estimation in images using CNNs trained with rendered 3D model views. In Proc. IEEE International Conference on Computer Vision 2686–2694 (IEEE, 2015).

5. Massa, F., Marlet, R. & Aubry, M. Crafting a multi-task CNN for viewpoint estimation. In Proc. British Machine Vision Conference 91.1–91.12 (BMVA, 2016).

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