1 citations · 2 across the 8 of their papers we have counts for
8 papers
Network Knowledge Prior Guided Learning for Data-Efficient Surface Defect Detection
Hang-Cheng Dong, Guodong Liu, Dong Ye +1
Deep learning-based methods have become the de facto standard for industrial defect detection. However, their data-hungry nature and inherent "black-box" characteristics often lead…
When Can We Trust Deep Neural Networks? Towards Reliable Industrial Deployment with an Interpretability Guide
Hang-Cheng Dong, Yuhao Jiang, Yibo Jiao +5
The deployment of AI systems in safety-critical domains, such as industrial defect inspection, autonomous driving, and medical diagnosis, is severely hampered by their lack of reli…
Sample-Centric Multi-Task Learning for Detection and Segmentation of Industrial Surface Defects
Hang-Cheng Dong, Yibo Jiao, Fupeng Wei +3
Industrial surface defect inspection for sample-wise quality control (QC) must simultaneously decide whether a given sample contains defects and localize those defects spatially. I…
Region-Aware CAM: High-Resolution Weakly-Supervised Defect Segmentation via Salient Region Perception
Hang-Cheng Dong, Lu Zou, Bingguo Liu +2
Surface defect detection plays a critical role in industrial quality inspection. Recent advances in artificial intelligence have significantly enhanced the automation level of dete…
Rethinking Class Activation Maps for Segmentation: Revealing Semantic Information in Shallow Layers by Reducing Noise
Hang-Cheng Dong, Yuhao Jiang, Yingyan Huang +4
Class activation maps are widely used for explaining deep neural networks. Due to its ability to highlight regions of interest, it has evolved in recent years as a key step in weak…
SAL-CNN: Estimate the Remaining Useful Life of Bearings Using Time-frequency Information
Bingguo Liu, Zhuo Gao, Binghui Lu +2
In modern industrial production, the prediction ability of the remaining useful life (RUL) of bearings directly affects the safety and stability of the system. Traditional methods…
Training Neural Networks for Solving 1-D Optimal Piecewise Linear Approximation
Hangcheng Dong, Jingxiao Liao, Yan Wang +4
Recently, the interpretability of deep learning has attracted a lot of attention. A plethora of methods have attempted to explain neural networks by feature visualization, saliency…
How to Explain Neural Networks: an Approximation Perspective
Hangcheng Dong, Bingguo Liu, Fengdong Chen +2
The lack of interpretability has hindered the large-scale adoption of AI technologies. However, the fundamental idea of interpretability, as well as how to put it into practice, re…