Vmem- phi: Low-Compute Out-of-Distribution Detection in Spiking Neural Networks from Membrane-Potential Statistics

arXiv:2610.00350 · cs.CV · Submitted 2026-09-29 · Read on arXiv

cs.CV

Submitted: 2026-09-29

Updated: 2026-09-29

Terminology

• AUROC — 27× in this paper · explained in 2 episodes
• Calibration — 25× in this paper · explained in 7 episodes
• Benchmark — 9× in this paper · explained in 2 episodes
• Aggregation — 7× in this paper · explained in 1 episode
• Robustness — 7× in this paper · explained in 5 episodes
• Ablation — 5× in this paper · explained in 1 episode
• Accuracy — 5× in this paper · explained in 1 episode
• Feature Extraction — 4× in this paper · explained in 2 episodes
• Multi-Descriptor Deviation — 4× in this paper
• In International Conference on Learning Representations — 3× in this paper
• Out-of-Distribution (OOD) Detection — 3× in this paper · explained in 2 episodes
• Alignment — 2× in this paper · explained in 1 episode
• Conference on Computer Vision and Pattern Recognition — 2× in this paper
• Latent Representation — 2× in this paper · explained in 2 episodes
• Latent Representations — 2× in this paper · explained in 3 episodes
• Spiking Neural Networks (SNNs) — 2× in this paper · explained in 3 episodes
• Abstention — 1× in this paper · explained in 1 episode
• Adaptation — 1× in this paper · explained in 1 episode
• Adversarial Attacks — 1× in this paper · explained in 1 episode
• Benchmarking — 1× in this paper · explained in 3 episodes
• Core — 1× in this paper · explained in 2 episodes
• Distribution Shift — 1× in this paper · explained in 6 episodes
• Explainability — 1× in this paper · explained in 3 episodes
• Model-Agnostic — 1× in this paper · explained in 2 episodes
• Most Out-of-Distribution — 1× in this paper
• Normalizing Flow — 1× in this paper · explained in 3 episodes
• Normalizing Flows — 1× in this paper · explained in 4 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• A Large Scale Event-based Detection Dataset for Automotive
• Out-of-Distribution Example Detection in Deep Neural Networks using Distance to Modelled Embedding
• Forward-Forward Learning achieves Highly Selective Latent Representations for Out-of-Distribution Detection in Fully Spiking Neural Networks

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