Confidence Calibration for Convolutional Neural Networks Using Structured Dropout

Understand

In classification applications, we often want probabilistic predictions to reflect confidence or uncertainty.

• Dropout, a commonly used training technique, has recently been linked to Bayesian inference, yielding an efficient way to quantify uncertainty in neural network models.
• However, as previously demonstrated, confidence estimates computed with a naive implementation of dropout can be poorly calibrated, particularly when using convolutional networks.
• In this paper, through the lens of ensemble learning, we associate calibration error with the correlation between the models sampled with dropout.