arXiv:2606.28654v1 Announce Type: cross Abstract: Deep Neural Network (DNN) classifiers suffer from poor calibration when their softmax outputs (predictive confidence) deviate from the empirical likelihoods. This manifests itself as either overconfident incorrect predictions or under-confident correct predictions. Label smoothing (LS) enhances model calibration by introducing entropy regularization during training through redistributing probability mass from the ground-truth label to the remaining classes. LS, including Margin-based LS (MbLS), have restrictive assumptions: they rely on predefi
The continuous development and deployment of DNNs across various critical applications necessitate improved reliability and trustworthiness, driving research into calibration methods.
Improved neural network calibration, ensuring predictive confidence aligns with accuracy, is crucial for trustworthy AI systems in sensitive domains like finance, healthcare, and autonomous systems.
This research introduces a novel label smoothing technique that dynamically adjusts based on feature modulation, potentially leading to more robust and accurate confidence estimates from AI models.
- · AI developers
- · Industries relying on AI predictions
- · Researchers in AI safety and reliability
- · AI models with poor calibration
- · Systems relying on overconfident or under-confident predictions
AI systems will become more reliable in their confidence estimates, reducing the risk of critical errors due to miscalibrated predictions.
Increased trust in AI systems could accelerate their adoption in highly regulated and safety-critical environments.
More explainable and trustworthy AI could lead to new regulatory frameworks emphasizing calibration as a key metric for deployment.
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Read at arXiv cs.LG