juniorAutoencoders
What is reconstruction loss in autoencoders?
Updated May 5, 2026
Short answer
Reconstruction loss measures how well the output matches the input.
Deep explanation
It is the difference between original input and reconstructed output. Common losses include MSE for continuous data and BCE for binary data. The goal is to minimize this loss during training.
Real-world example
Used in anomaly detection where high reconstruction error indicates anomalies.
Common mistakes
- Using wrong loss function for data type.
Follow-up questions
- Why is reconstruction important?
- Can loss be zero?