Enhancing Neural Network Interpretability Through Deep Prior-Guided Expected Gradients
The increasing adoption of DNNs in critical domains such as healthcare, finance, and autonomous systems underscores the growing importance of explainable artificial intelligence (XAI). In these high-stakes applications, understanding the decision-making processes of models is essential for ensuring...
Saved in:
| Main Authors: | Su-Ying Guo, Xiu-Jun Gong |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2025-06-01
|
| Series: | Applied Sciences |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2076-3417/15/13/7090 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Blind Infrared Remote-Sensing Image Deblurring Algorithm via Edge Composite-Gradient Feature Prior and Detail Maintenance
by: Xiaohang Zhao, et al.
Published: (2024-12-01) -
Class Activation Map Guided Backpropagation for Discriminative Explanations
by: Yongjie Liu, et al.
Published: (2025-01-01) -
Automated Grading Through Contrastive Learning: A Gradient Analysis and Feature Ablation Approach
by: Mateo Sokač, et al.
Published: (2025-04-01) -
Challenges and Perspectives in Interpretable Music Auto-Tagging Using Perceptual Features
by: Vassilis Lyberatos, et al.
Published: (2025-01-01) -
TF-LIME : Interpretation Method for Time-Series Models Based on Time–Frequency Features
by: Jiazhan Wang, et al.
Published: (2025-04-01)