Compressing Deep Neural Networks Using Explainable AI
2025-07-09
Summary
The article introduces a new deep neural network (DNN) compression technique that leverages explainable AI (XAI) methods, specifically Layer-wise Relevance Propagation (LRP), to effectively reduce model size without sacrificing accuracy. By computing importance scores for DNN parameters, the approach prunes unimportant parameters and applies mixed-precision quantization to retain only the most crucial components, achieving a 64% reduction in model size and a 42% increase in accuracy compared to existing XAI-based methods.
Why This Matters
As DNNs become increasingly prevalent across various devices, their large size and high computational demands pose challenges, especially for resource-constrained environments like mobile and embedded systems. This novel compression technique addresses these limitations, allowing DNNs to be run efficiently on devices with limited memory and power, thus broadening the applicability of AI technologies.
How You Can Use This Info
Professionals in industries reliant on AI can adopt this LRP-based compression technique to optimize DNNs for deployment on smaller devices without compromising performance. This approach is particularly beneficial for applications in IoT, mobile computing, and edge AI, where efficient use of resources is critical. Additionally, understanding the importance of different network components through XAI can lead to more transparent and interpretable AI solutions.