Frequency Regularization: Reducing Information Redundancy in Convolutional Neural Networks

1 code implementation • IEEE Access 2023 • Chenqiu Zhao, Guanfang Dong, Shupei Zhang, Zijie Tan, Anup Basu

Since high-frequency components of images are known to be less critical, a large proportion of these parameters can be set to zero when networks are trained with the proposed frequency regularization.

Frequency Regularization: Restricting Information Redundancy of Convolutional Neural Networks

1 code implementation • 17 Apr 2023 • Chenqiu Zhao, Guanfang Dong, Shupei Zhang, Zijie Tan, Anup Basu

Since high frequency components of images are known to be less critical, a large proportion of these parameters can be set to zero when networks are trained with the proposed frequency regularization.

Conditional Uncorrelation and Efficient Non-approximate Subset Selection in Sparse Regression

no code implementations • 8 Sep 2020 • Jianji Wang, Qi Liu, Shupei Zhang, Nanning Zheng, Fei-Yue Wang

By the proposed method, the computational complexity is reduced from $O(\frac{1}{6}{k^3}+mk^2+mkd)$ to $O(\frac{1}{6}{k^3}+\frac{1}{2}mk^2)$ for each candidate subset in sparse regression.

regression