Learning lightweight super-resolution networks with weight pruning

Author:

Jiang Xinrui,Wang NannanORCID,Xin Jingwei,Xia XiaoboORCID,Yang Xi,Gao XinboORCID

Publisher

Elsevier BV

Subject

Artificial Intelligence,Cognitive Neuroscience

Reference47 articles.

1. Net-trim: Convex pruning of deep neural networks with performance guarantee;Aghasi,2017

2. Ahn, N., Kang, B., & Sohn, K.-A. (2018). Fast, accurate, and lightweight super-resolution with cascading residual network. In Proceedings of the european conference on computer vision.

3. Low-complexity single-image super-resolution based on nonnegative neighbor embedding;Bevilacqua,2012

4. Fast, accurate and lightweight super-resolution with neural architecture search;Chu,2019

5. Dai, T., Cai, J., Zhang, Y., Xia, S.-T., & Zhang, L. (2019). Second-order attention network for single image super-resolution. In Proceedings of the ieee conference on computer vision and pattern recognition (pp. 11065–11074).

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