Explain the use of Autoencoders for detecting anomalies.
Updated May 5, 2026
Short answer
Anomalies are flagged by high reconstruction error[cite: 1].
Deep explanation
The model learns to compress and reconstruct 'normal' data. Because it hasn't seen anomalies, it fails to reconstruct them accurately[cite: 1].
Real-world example
Image-based quality control on assembly lines[cite: 1].
Common mistakes
- Using a bottleneck that is too large (model learns identity)[cite: 1].
Follow-up questions
- What loss function is used?