DasNet: Dynamic Adaptive Structure for Accelerating Multi-task Convolutional Neural Network-Reference-Cited by-同舟云学术

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DasNet: Dynamic Adaptive Structure for Accelerating Multi-task Convolutional Neural Network

Published:2019 Issue: Volume: Page:138-150
ISSN:0302-9743
Container-title:Neural Information Processing
language:
Short-container-title:

Author:

Ma Yinan,Wu Jing,Long Chengnian

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-36708-4_12

Reference26 articles.

1. Teichmann, M., Weber, M., Zoellner, M., Cipolla, R., Urtasun, R.: MultiNet: real-time joint semantic reasoning for autonomous driving. In: IEEE Intelligent Vehicles Symposium, pp. 1013–1020 (2018)

2. Kokkinos, I.: UberNet: training a universal convolutional neural network for low-, mid-, and high-level vision using diverse datasets and limited memory. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 5454–5463 (2017)

3. Misra, I., Shrivastava, A., Gupta, A., Hebert, M.: Cross-stitch networks for multi-task learning. In: IEEE Conference on Computer Vision and Pattern Recognition, pp. 3994–4003 (2016)

4. Jou, B., Chang, S.-F.: Deep cross residual learning for multitask visual recognition. In: Proceedings of the 24th ACM International Conference on Multimedia, Amsterdam, pp. 998–1007 (2016)

5. Aljundi, R., Chakravarty, P., Tuytelaars, T.: Expert gate: lifelong learning with a network of experts. In: The IEEE Conference on Computer Vision and Pattern Recognition, pp. 3366–3375 (2017)

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