arXiv:2505.23866v2 Announce Type: replace Abstract: Deep neural networks have been increasingly used in safety-critical applications such as medical diagnosis and autonomous driving. However, many studies suggest that they are prone to being poorly calibrated and have a propensity for overconfidence, which may have disastrous consequences. In this paper, unlike standard training such as stochastic gradient descent, we show that the recently proposed sharpness-aware minimization (SAM) counteracts this tendency towards overconfidence. The theoretical analysis suggests that SAM allows us to learn
The increasing deployment of deep neural networks in safety-critical applications necessitates improved reliability and trustworthiness, leading to a focus on calibration techniques like SAM.
Improving AI model calibration and reducing overconfidence is critical for the safe and effective integration of AI into sensitive domains, mitigating potential catastrophic failures.
This research highlights a method to make advanced AI models more trustworthy and less prone to overconfidence, directly impacting their real-world applicability.
- · AI developers
- · Healthcare sector
- · Autonomous vehicle developers
- · AI ethics and safety researchers
- · Developers relying solely on standard training methods
- · Applications with high-consequence failure modes due to overconfident AI
Improved trust and adoption of deep neural networks in high-stakes fields.
Reduced regulatory hurdles for AI deployment as models become demonstrably more reliable.
Accelerated development of fully autonomous systems with enhanced safety guarantees.
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Read at arXiv cs.LG