I am confused about dropout. If I am using dropout to prevent overfitting during training, do I need to keep it on during inference? I keep reading conflicting advice online. Some people say turn it off, others suggest keeping it on for uncertainty estimation. What is the standard practice for a production-ready model?
Dropout is disabled at test time to ensure deterministic, reproducible model predictions, except in specific cases where Monte Carlo dropout is used to estimate predictive uncertainty.
1 answer
For standard production environments, you must disable dropout during inference to ensure deterministic output. If you maintain dropout at test time, you introduce stochastic noise that complicates model validation and reproducibility, which is unacceptable when working with regulatory standards or fixed performance benchmarks.
