arXiv Computer Vision
Beyond Reconstruction: Reconstruction-to-Vector Diffusion for Hyperspectral Anomaly Detection
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When Does Unsupervised Learning Succeed or Fail? A PoS Perspective on Reconstruction-Based Anomaly Detection
The paper investigates why reconstruction-based unsupervised learning can fail, identifying two failure modes: over‑reconstruction of anomalies and loss of nominal variation. Using the Pursuit of Subspaces hypothesis, it links these failures to geometric properties—join blindness from excess range and meet preference from insufficient capacity—and shows that a compact nominal union is optimal, typically requiring a nonlinear reconstruction map. The authors propose Dynamic Push and Pull, along with nested manifold carving, to learn compact representations without anomaly labels, and demonstrate improved anomaly detection on standard benchmarks, unseen image degradations, and ECG classification.