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English(EN) HumP-KD: A Hybrid Uncertainty-Aware Multi-Stage Progressive Knowledge Distillation Framework for Efficient Fire Classification

新的HumP-KD框架可高效蒸馏火灾分类模型

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员开发了HumP-KD，一个使用知识蒸馏进行高效火灾分类的新框架。该方法将来自Swin-Tiny和ViT-Base等大型Transformer模型的知识蒸馏到一个小型、轻量级的MobileViT-S学生模型中。该框架在Dataset-II上取得了0.9876的高F1分数，显著优于基线学生模型，同时保持了适合实时部署的紧凑尺寸和高处理速度。 AI

影响 实现了更高效、可部署的AI模型，用于资源受限硬件上的实时分类任务。

排序理由 该集群描述了一篇详细介绍特定机器学习任务新框架的学术论文。

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

新的HumP-KD框架可高效蒸馏火灾分类模型

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报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Mohammed Arif Mainuddin, Najifa Tabassum, Omar Ibne Shahid, Riasat Khan ·

    HumP-KD：一种混合不确定性感知多阶段渐进式知识蒸馏框架，用于高效火灾分类

    arXiv:2606.14684v1 Announce Type: cross Abstract: Real-time fire classification systems require models that are simultaneously accurate, computationally efficient, and deployable on resource-constrained hardware. This work proposes \textbf{HumP-KD}, a Hybrid Uncertainty-aware Mul…

  2. arXiv cs.CV TIER_1 English(EN) · Riasat Khan ·

    HumP-KD：一种混合不确定性感知多阶段渐进式知识蒸馏框架，用于高效火灾分类

    Real-time fire classification systems require models that are simultaneously accurate, computationally efficient, and deployable on resource-constrained hardware. This work proposes \textbf{HumP-KD}, a Hybrid Uncertainty-aware Multi-stage Progressive Knowledge Distillation framew…

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