arXiv:2604.25649v2 Announce Type: replace Abstract: Deep learning models are used in critical applications, in which mistakes can have serious consequences. Therefore, it is crucial to understand how and why models generate predictions. This understanding provides useful information to check whether the model is learning the right patterns, detect biases in the data, improve model design, and build systems that can be trusted. This work proposes a new method for interpreting Convolutional Neural Networks in image classification tasks. The approach works by selecting the most representative fea
The increasing deployment of deep learning models in critical applications necessitates methods for interpretability, driving research into techniques like quantum annealing for feature selection.
Improved AI interpretability is crucial for building trust, detecting biases, and ensuring models are learning correct patterns, which affects the ethical and safe deployment of AI systems.
This new method offers a potential pathway to better understand the decisions of complex AI models, particularly in image classification, which could lead to more robust and explainable AI.
- · AI developers
- · Ethical AI advocates
- · Industries using critical AI systems
- · Quantum computing providers
- · Opaque AI systems
- · Companies with biased AI models
The adoption of quantum annealing in feature selection could lead to more transparent deep learning models.
Increased transparency and interpretability in AI could accelerate its deployment in highly regulated or sensitive sectors.
General public trust in AI systems may gradually increase as interpretability improves, potentially influencing broader AI adoption and policy.
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Read at arXiv cs.LG