arXiv (All)
open access
Vmem-$φ$: Low-Compute Out-of-Distribution Detection in Spiking Neural Networks from Membrane-Potential Statistics
Spiking Neural Networks (SNNs) offer an energy-efficient approach to processing event-camera data, yet out-of-distribution (OOD) detection remains challenging in this setting. Existing OOD detection methods often depend on model outputs or computational components that are unavailable in object dete
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- MAS-SNN: Adaptive Sampling and Representation via Memory-Augmented Recurrent Spiking Neural Networks for Event-Based Object Detection#381279
- Can Spiking Neural Networks play pinball? A neuromorphic motion detector for target tracking#1050582
- Unsupervised spiking feature learning for event-based pedestrian crossing detection: approaching supervised accuracy without labelled training data#1115627
- FORGE: Forward-Only Test-Time Adaptation for Integer-Only Vision Models on Microcontrollers#822117
- Controllable Stochastic Quantization Encoding for Adversarially Robust Spiking Neural Networks#1156830
Record · ID 1154973
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive