Binarized Weight Networks for Inverse Problems

Date

2020-01-01

Author

Ozkan, Savas
Becek, Kadircan
Inci, Alperen
Kutukcu, Basar
Ugurcali, Faruk
Kaya, Mete Can
Akar, Gözde

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This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

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In this paper, we present a binarized neural network structure for inverse problems. In this structure, memory requirements and computation time are significantly reduced with a negligible performance drop compared to full-precision models. For this purpose, a unique architecture is proposed based on a residual learning. Precisely, it opts to reconstruct only the error between input and output images, which is eventually centralized the responses around zero. To this end, this provides several advantages for binary representation and manifold space is adopted to learn with binarized networks. Experiments are conducted on three different inverse problems as super-resolution, denoising and deblurring problems for various datasets. The results validate the success of the method.

Subject Keywords

Inverse problems, Super-Resolution, Denoising, Deblurring, Binary Networks

URI

https://hdl.handle.net/11511/96529

DOI

https://doi.org/10.1109/siu49456.2020.9302129

Conference Name

28th Signal Processing and Communications Applications Conference (SIU)

Collections

Department of Electrical and Electronics Engineering, Conference / Seminar

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Citation Formats

S. Ozkan et al., “Binarized Weight Networks for Inverse Problems,” presented at the 28th Signal Processing and Communications Applications Conference (SIU), ELECTR NETWORK, 2020, Accessed: 00, 2022. [Online]. Available: https://hdl.handle.net/11511/96529.