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arXiv Machine Learning By Mohammed Ayalew Belay, Amirshayan Haghipour, Pierluigi Salvo Rossi

Robust Prototypical Networks for Few-Shot Sensor Fault Diagnosis

Read the original on arXiv Machine Learning →

The paper introduces Multi-Episode Prototypical Networks (MEPN), a variant of prototypical networks that aggregates class prototypes from multiple disjoint support episodes to reduce prototype variance in few-shot sensor fault diagnosis. MEPN is evaluated on the DeFACTO sensor dataset with synthetic fault injections, achieving significantly higher one-shot accuracy than single-episode baselines while matching ProtoNet performance under a 10-sample support budget. The approach demonstrates that prototype accumulation improves stability without altering the encoder architecture.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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