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New RefCal framework boosts deep neural network reliability

By PulseAugur Editorial · [2 sources] ·

Researchers have developed a new framework called RefCal to improve the reliability of deep neural networks. This framework jointly optimizes accuracy, calibration, and refinement, addressing the common issue where improving calibration can degrade refinement. RefCal utilizes a novel loss function based on supervised contrastive learning to explicitly promote refinement. In tests on the CIFAR-100-LT dataset, RefCal significantly outperformed existing methods in accuracy, refinement, and expected calibration error. AI

IMPACT Enhances DNN reliability by improving confidence estimates, potentially increasing user trust in AI systems.

RANK_REASON The cluster contains an academic paper detailing a new method for improving deep neural network reliability.

Read on arXiv cs.AI →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

COVERAGE [2]

  1. arXiv cs.AI TIER_1 English(EN) · Ramya Hebbalaguppe, Ajay Shastry, Soumya Suvra Ghosal, Chetan Arora ·

    Enhancing Deep Neural Network Reliability with Refinement and Calibration

    arXiv:2605.23249v1 Announce Type: cross Abstract: Although deep neural networks (DNNs) achieve high predictive accuracy, their confidence estimates are often unreliable, potentially compromising user trust in their decisions. This has motivated research on calibrated models, wher…

  2. arXiv cs.AI TIER_1 English(EN) · Chetan Arora ·

    Enhancing Deep Neural Network Reliability with Refinement and Calibration

    Although deep neural networks (DNNs) achieve high predictive accuracy, their confidence estimates are often unreliable, potentially compromising user trust in their decisions. This has motivated research on calibrated models, where calibration measures how well a model's predicte…

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