Visual-TCAV: Concept-based Attribution and Saliency Maps for Post-hoc Explainability in Image Classification
arXiv:2411.05698v3 Announce Type: replace-cross Abstract: Convolutional Neural Networks (CNNs) have shown remarkable performance in image classification. However, interpreting their predictions is challenging due to the size and complexity of these models. State-of-the-art saliency methods generate local explanations highlighting the area in the input image where a class is identified but cannot explain how a concept of interest contributes to the prediction. On the other hand, concept-based methods, such as TCAV, provide insights into how sensitive the network is to a human-defined concept bu
The increasing complexity of AI models, particularly in image classification, necessitates more transparent and interpretable explanations for their decision-making processes.
Improved explainability in AI models is critical for trust, debugging, regulatory compliance, and accelerating AI adoption in sensitive applications like healthcare and autonomous systems.
This advancement enables developers and users to understand not just 'what' an AI sees, but 'how' it interprets concepts, moving beyond simple saliency maps to concept-level attribution.
- · AI developers
- · AI ethicists
- · Industries using vision AI
- · Regulatory bodies
- · Black-box AI models
- · Companies relying on opaque AI
Increased adoption and trust in AI systems due to better explainability.
Faster development and deployment of more robust and auditable AI applications across various sectors.
Potentially democratizes AI design by allowing non-experts to better understand and contribute to model interpretation.
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Read at arXiv cs.LG