publication . Other literature type . Preprint . Article . 2019

Adaptive importance learning for improving lightweight image super-resolution network

Lei Zhang; Peng Wang; Chunhua Shen; Lingqiao Liu; Wei Wei; Yanning Zhang; Anton van den Hengel;
Open Access
  • Published: 06 Nov 2019
  • Publisher: Springer Science and Business Media LLC
Abstract
Deep neural networks have achieved remarkable success in single image super-resolution (SISR). The computing and memory requirements of these methods have hindered their application to broad classes of real devices with limited computing power, however. One approach to this problem has been lightweight network architectures that bal- ance the super-resolution performance and the computation burden. In this study, we revisit this problem from an orthog- onal view, and propose a novel learning strategy to maxi- mize the pixel-wise fitting capacity of a given lightweight network architecture. Considering that the initial capacity of the lightweight network is very ...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Software, Artificial Intelligence, Computer Vision and Pattern Recognition
Funded by
ARC| Continuously learning to see
Project
  • Funder: Australian Research Council (ARC) (ARC)
  • Project Code: FT120100969
  • Funding stream: Future Fellowships
34 references, page 1 of 3

Basu S, Christensen J (2013) Teaching classification boundaries to humans. In: AAAI

Bengio Y, Louradour J, Collobert R, Weston J (2009) Curriculum learning. In: Proceedings of the 26th annual international conference on machine learning, ACM, pp 41-48 [OpenAIRE]

Bevilacqua M, Roumy A, Guillemot C, Alberi-Morel ML (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding [OpenAIRE]

Dong C, Loy CC, He K, Tang X (2016a) Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence 38(2):295-307

Dong C, Loy CC, Tang X (2016b) Accelerating the super-resolution convolutional neural network. In: European Conference on Computer Vision, Springer, pp 391-407

Efrat N, Glasner D, Apartsin A, Nadler B, Levin A (2013) Accurate blur models vs. image priors in single image super-resolution. In: Computer Vision (ICCV), 2013 IEEE International Conference on, IEEE, pp 2832-2839

Glasner D, Bagon S, Irani M (2009) Super-resolution from a single image. In: Computer Vision, 2009 IEEE 12th International Conference on, IEEE, pp 349-356 [OpenAIRE]

Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:150302531

Huang JB, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5197- 5206

Jiang L, Meng D, Mitamura T, Hauptmann AG (2014) Easy samples first: Self-paced reranking for zero-example multimedia search. In: Proceedings of the 22nd ACM international conference on Multimedia, ACM, pp 547-556

Khan F, Mutlu B, Zhu X (2011) How do humans teach: On curriculum learning and teaching dimension. In: Advances in Neural Information Processing Systems, pp 1449-1457

Kim J, Kwon Lee J, Mu Lee K (2016a) Accurate image superresolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1646-1654

Kim J, Kwon Lee J, Mu Lee K (2016b) Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1637- 1645

Kim KI, Kwon Y (2010) Single-image super-resolution using sparse regression and natural image prior. IEEE transactions on pattern analysis and machine intelligence 32(6):1127-1133

Lai WS, Huang JB, Ahuja N, Yang MH (2017) Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 624-632

34 references, page 1 of 3
Abstract
Deep neural networks have achieved remarkable success in single image super-resolution (SISR). The computing and memory requirements of these methods have hindered their application to broad classes of real devices with limited computing power, however. One approach to this problem has been lightweight network architectures that bal- ance the super-resolution performance and the computation burden. In this study, we revisit this problem from an orthog- onal view, and propose a novel learning strategy to maxi- mize the pixel-wise fitting capacity of a given lightweight network architecture. Considering that the initial capacity of the lightweight network is very ...
Subjects
free text keywords: Computer Science - Computer Vision and Pattern Recognition, Software, Artificial Intelligence, Computer Vision and Pattern Recognition
Funded by
ARC| Continuously learning to see
Project
  • Funder: Australian Research Council (ARC) (ARC)
  • Project Code: FT120100969
  • Funding stream: Future Fellowships
34 references, page 1 of 3

Basu S, Christensen J (2013) Teaching classification boundaries to humans. In: AAAI

Bengio Y, Louradour J, Collobert R, Weston J (2009) Curriculum learning. In: Proceedings of the 26th annual international conference on machine learning, ACM, pp 41-48 [OpenAIRE]

Bevilacqua M, Roumy A, Guillemot C, Alberi-Morel ML (2012) Low-complexity single-image super-resolution based on nonnegative neighbor embedding [OpenAIRE]

Dong C, Loy CC, He K, Tang X (2016a) Image super-resolution using deep convolutional networks. IEEE transactions on pattern analysis and machine intelligence 38(2):295-307

Dong C, Loy CC, Tang X (2016b) Accelerating the super-resolution convolutional neural network. In: European Conference on Computer Vision, Springer, pp 391-407

Efrat N, Glasner D, Apartsin A, Nadler B, Levin A (2013) Accurate blur models vs. image priors in single image super-resolution. In: Computer Vision (ICCV), 2013 IEEE International Conference on, IEEE, pp 2832-2839

Glasner D, Bagon S, Irani M (2009) Super-resolution from a single image. In: Computer Vision, 2009 IEEE 12th International Conference on, IEEE, pp 349-356 [OpenAIRE]

Hinton G, Vinyals O, Dean J (2015) Distilling the knowledge in a neural network. arXiv preprint arXiv:150302531

Huang JB, Singh A, Ahuja N (2015) Single image super-resolution from transformed self-exemplars. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 5197- 5206

Jiang L, Meng D, Mitamura T, Hauptmann AG (2014) Easy samples first: Self-paced reranking for zero-example multimedia search. In: Proceedings of the 22nd ACM international conference on Multimedia, ACM, pp 547-556

Khan F, Mutlu B, Zhu X (2011) How do humans teach: On curriculum learning and teaching dimension. In: Advances in Neural Information Processing Systems, pp 1449-1457

Kim J, Kwon Lee J, Mu Lee K (2016a) Accurate image superresolution using very deep convolutional networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp 1646-1654

Kim J, Kwon Lee J, Mu Lee K (2016b) Deeply-recursive convolutional network for image super-resolution. In: Proceedings of the IEEE conference on computer vision and pattern recognition, pp 1637- 1645

Kim KI, Kwon Y (2010) Single-image super-resolution using sparse regression and natural image prior. IEEE transactions on pattern analysis and machine intelligence 32(6):1127-1133

Lai WS, Huang JB, Ahuja N, Yang MH (2017) Deep laplacian pyramid networks for fast and accurate super-resolution. In: Proc. IEEE Conf. Comput. Vis. Pattern Recognit., pp 624-632

34 references, page 1 of 3
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