no code implementations • 1 Mar 2024 • Zhengyu Zhang, Ruisi He, Bo Ai, Mi Yang, Yong Niu, Zhangdui Zhong, Yujian Li, Xuejian Zhang, Jing Li
However, some important features of ISAC channels have not been well characterized, for example, most existing ISAC channel models consider communication channels and sensing channels independently, whereas ignoring correlation under the consistent environment.
no code implementations • 9 Nov 2023 • Shiqi Jiang, Junjie Kang, Yujian Li
The color and texture dual pipeline architecture (CTDP) suppresses texture representation and artifacts through masked total variation loss (Mtv), and further experiments have shown that smooth input can almost completely eliminate texture representation.
no code implementations • 10 Jan 2020 • Yujian Li, Bowen Liu, Zhaoying Liu, Ting Zhang
In theory, we can solve the model by active gradient projection, while inefficiently.
1 code implementation • CVPR 2019 • Jiashi Li, Qi Qi, Jingyu Wang, Ce Ge, Yujian Li, Zhangzhang Yue, Haifeng Sun
Many channel pruning works utilize structured sparsity regularization to zero out all the weights in some channels and automatically obtain structure-sparse network in training stage.
no code implementations • 7 Mar 2019 • Yujian Li, Chuanhui Shan
Recently, the growth of deep learning has produced a large number of deep neural networks.
no code implementations • 1 Dec 2018 • Yujian Li
Rather than brain-like intelligence, the TCR indeed advocates a promising change in direction to realize true AI, i. e. artificial general intelligence or artificial consciousness, particularly different from humans' and animals'.
no code implementations • 9 May 2018 • Yujian Li, Chuanhui Shan
With the growth of deep learning, how to describe deep neural networks unifiedly is becoming an important issue.
no code implementations • 7 Oct 2017 • Yujian Li
People can think in auditory, visual and tactile forms of language, so can machines principally.
no code implementations • 3 Oct 2017 • Yujian Li, Ting Zhang, Zhaoying Liu, Haihe Hu
It is well accepted that convolutional neural networks play an important role in learning excellent features for image classification and recognition.