Partition of Unity Neural Networks for Interpretable Classification with Explicit Class Regions
arXiv:2602.00511v2 Announce Type: replace Abstract: Despite their empirical success, neural network classifiers remain difficult to interpret. In softmax-based models, class regions are defined implicitly as solutions to systems of inequalities among logits, making them difficult to extract and visualize. We introduce Partition of Unity Neural Networks (PUNN), an architecture in which class probabilities arise directly from a learned partition of unity, without requiring a softmax layer. PUNN constructs $k$ nonnegative functions $h_1, \ldots, h_k$ satisfying $\sum_i h_i(x) = 1$, where each $h_
The continuous drive for more transparent and reliable AI systems fuels research into interpretable neural network architectures, addressing a key limitation of current deep learning models.
Improved interpretability in AI allows for greater trust, easier debugging, and regulatory compliance, expanding the potential applications of neural networks in critical fields.
This research introduces a novel neural network design that inherently provides explicit class regions, offering a more direct and understandable basis for classification decisions compared to opaque softmax-based models.
- · AI researchers
- · Developers of safety-critical AI
- · Sectors requiring explainable AI (e.g., healthcare, finance)
- · Black-box AI model developers
- · Traditional softmax-based interpretability tools
PUNN offers a new paradigm for building neural networks with built-in interpretability from their architectural design.
Wider adoption of such interpretable models could lead to new regulatory standards for AI transparency and explainability.
Increased trust in AI systems could accelerate their integration into highly sensitive and autonomous decision-making processes, potentially shifting legal liabilities.
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