arXiv Machine Learning June 15, 2026 By Mohammed Arif Mainuddin, Najifa Tabassum, Omar Ibne Shahid, Riasat Khan

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

Read the original on arXiv Machine Learning →

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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv Machine Learning.

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