A Comparative Study of Variational Autoencoders, Normalizing Flows, and Score-based Diffusion Models for Electrical Impedance Tomography

1 code implementation • 24 Oct 2023 • Huihui Wang, Guixian Xu, Qingping Zhou

This study aims to investigate the potential of three DGMs-variational autoencoder networks, normalizing flow, and score-based diffusion model-to learn implicit regularizers in learning-based EIT imaging.

Image Reconstruction

Enhancing Electrical Impedance Tomography reconstruction using Learned Half-Quadratic Splitting Networks with Anderson Acceleration

no code implementations • 16 Apr 2023 • Guixian Xu, Huihui Wang, Qingping Zhou

Our Anderson acceleration scheme to enhance HQSNet is generic and can be applied to improve the performance of various physics-embedded deep learning methods.

Image Reconstruction Medical Diagnosis

WSSS4LUAD: Grand Challenge on Weakly-supervised Tissue Semantic Segmentation for Lung Adenocarcinoma

no code implementations • 13 Apr 2022 • Chu Han, Xipeng Pan, Lixu Yan, Huan Lin, Bingbing Li, Su Yao, Shanshan Lv, Zhenwei Shi, Jinhai Mai, Jiatai Lin, Bingchao Zhao, Zeyan Xu, Zhizhen Wang, Yumeng Wang, Yuan Zhang, Huihui Wang, Chao Zhu, Chunhui Lin, Lijian Mao, Min Wu, Luwen Duan, Jingsong Zhu, Dong Hu, Zijie Fang, Yang Chen, Yongbing Zhang, Yi Li, Yiwen Zou, Yiduo Yu, Xiaomeng Li, Haiming Li, Yanfen Cui, Guoqiang Han, Yan Xu, Jun Xu, Huihua Yang, Chunming Li, Zhenbing Liu, Cheng Lu, Xin Chen, Changhong Liang, Qingling Zhang, Zaiyi Liu

According to the technical reports of the top-tier teams, CAM is still the most popular approach in WSSS.

Data Augmentation Weakly supervised Semantic Segmentation +1

Online Attentive Kernel-Based Temporal Difference Learning

no code implementations • 22 Jan 2022 • Guang Yang, Xingguo Chen, Shangdong Yang, Huihui Wang, Shaokang Dong, Yang Gao

Moreover, in learning sparse representations, attention mechanisms are utilized to represent the degree of sparsification, and a smooth attentive function is introduced into the kernel-based VFA.

Acrobot Reinforcement Learning (RL)

Neural network algorithm and its application in reactive distillation

no code implementations • 16 Nov 2020 • Huihui Wang, Ruyang Mo

This paper briefly describes the characteristics and research progress of reactive distillation technology and neural network algorithms, and summarizes the application of neural network algorithms in reactive distillation, aiming to provide reference for the development and innovation of industry technology.