Embarrassingly Simple Binarization for Deep Single Imagery Super-Resolution Networks
Published in TIP, 2021
This paper present an embarrassingly simple but effective binarization scheme for SISR, which can obviously relieve the performance degeneration resulted from network binarization and is applicable to different DCNN architectures. Specifically, we force each weight to follow a compact uniform prior, with which the weight will be given a very small absolute value close to zero and its binarization result can be straightforwardly reversed even by a small backpropagated gradient. By doing this, the flexibility and the generalization performance of the binarized network can be improved.
Recommended citation: L. Zhang, Z. Lang, W. Wei and Y. Zhang, "Embarrassingly Simple Binarization for Deep Single Imagery Super-Resolution Networks," in IEEE Transactions on Image Processing, vol. 30, pp. 3934-3945, 2021, doi: 10.1109/TIP.2021.3066906. https://ieeexplore.ieee.org/document/9384273