Manifolds for Unsupervised Visual Anomaly Detection

TL;DR

This paper introduces a novel manifold-based variational auto-encoder for unsupervised visual anomaly detection, achieving state-of-the-art results in industrial and medical imaging by modeling data distributions more effectively.

Contribution

It proposes constant curvature manifolds and a hyperspherical VAE with stereographic projections, enhancing generalization and interpretability in anomaly detection.

Findings

State-of-the-art results on industrial benchmarks

Effective detection of cancerous tissue in histopathology

Improved interpretability of anomaly representations

Abstract

Anomalies are by definition rare, thus labeled examples are very limited or nonexistent, and likely do not cover unforeseen scenarios. Unsupervised learning methods that don't necessarily encounter anomalies in training would be immensely useful. Generative vision models can be useful in this regard but do not sufficiently represent normal and abnormal data distributions. To this end, we propose constant curvature manifolds for embedding data distributions in unsupervised visual anomaly detection. Through theoretical and empirical explorations of manifold shapes, we develop a novel hyperspherical Variational Auto-Encoder (VAE) via stereographic projections with a gyroplane layer - a complete equivalent to the Poincar\'e VAE. This approach with manifold projections is beneficial in terms of model generalization and can yield more interpretable representations. We present state-of-the-art results on visual anomaly benchmarks in precision manufacturing and inspection, demonstrating real-world utility in industrial AI scenarios. We further demonstrate the approach on the challenging problem of histopathology: our unsupervised approach effectively detects cancerous brain tissue from noisy whole-slide images, learning a smooth, latent organization of tissue types that provides an interpretable decisions tool for medical professionals.