arXiv:2507.09092v2 Announce Type: replace-cross Abstract: With the intervention of machine vision in our crucial day to day necessities including healthcare and automated power plants, attention has been drawn to the internal mechanisms of convolutional neural networks, and the reason why the network provides specific inferences. This paper proposes a novel post-hoc visual explanation method called MI CAM based on activation mapping. Differing from previous class activation mapping based approaches, MI CAM produces saliency visualizations by weighing each feature map through its mutual informa
The increasing deployment of AI in critical sectors necessitates greater transparency and trustworthiness, pushing research into explainable AI techniques.
Improving the explainability of AI models, particularly in domains like healthcare and automated systems, is crucial for adoption, regulatory compliance, and building public trust.
This research introduces a new method for visual explanation in AI, potentially leading to more accurate and reliable interpretations of why AI models make specific decisions, particularly for convolutional neural networks.
- · AI developers
- · Healthcare sector
- · Automated systems industry
- · Regulatory bodies
- · Opaque AI systems
- · Companies with poor AI explainability practices
Improved understanding of deep learning models will accelerate AI development and integration into sensitive applications.
Greater interpretability could lead to new regulatory frameworks and industry standards for AI transparency and accountability.
Public confidence in AI could significantly increase, fostering broader societal acceptance and reliance on autonomous systems.
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Read at arXiv cs.LG