Self-Supervised Learning for Anomaly Detection

Self-Supervised Learning (SSL) for Anomaly Detection is the training of models on only normal (defect-free) data using self-supervised tasks — the model learns the distribution of normal patterns, and anything that deviates from the learned normality is flagged as an anomaly.

Key SSL Approaches for Anomaly Detection

  • Autoencoders: Learn to reconstruct normal images. Anomalies have high reconstruction error.
  • Contrastive Learning: Learn representations of normal data. Anomalies have distant embeddings.
  • Knowledge Distillation: Student network trained on normal data disagrees with teacher on anomalies.
  • Masked Image Modeling: Predict masked regions — anomalies are poorly predicted.

Why It Matters

  • No Defect Labels Needed: Only requires normal (good) images for training — defect labels are expensive and rare.
  • Novel Defects: Detects previously unseen defect types (anything abnormal), not just known categories.
  • Industrial Standard: Approaches like PatchCore and FastFlow achieve >99% AUROC on industrial anomaly benchmarks.

SSL for Anomaly Detection is learning what normal looks like — training exclusively on good data so that any deviation is automatically flagged as suspicious.

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