SOURCE-LINKED INTELLIGENCE

Differentiable Interval Bottlenecks for Interpretable Anomaly Detection in Numerical Data

arXiv · AI, language, vision and robotics · article · Sep 3, 2026 · UTC

Reconstruction-based anomaly detectors are accurate but opaque: a deep autoencoder flags a sample without telling a practitioner which feature ranges made it anomalous. We propose DIFFINT, an autoencoder whose latent bottleneck is structured as a set of soft, axis-aligned interval memberships learned end-to-end directly from raw numerical data, without any discretization or binarization. Each latent unit corresponds to a human-readable hyper-rectangle in feature space; an instance is encoded by how strongly it falls inside each interval relative to the other units, and its reconstruction error

Read original source ↗ Open in workspace

recordType

paper

region

Global