Distribution Shift

Detect Distribution Shift with Energy Scores

A classifier can assign a high softmax probability to an input that does not resemble the data used to fit it. The probability vector still has to sum to one, so normalization can produce a confident-looking prediction even when every class is a poor match. An energy score provides a scalar derived from the logits before that normalization and can serve as a signal for out-of-distribution detection. The score does not make a classifier aware of every possible unfamiliar input. Its value depends on the model, logit scale, training procedure, and data used to set a decision threshold. That makes energy-based detection an evaluation problem as much as a scoring mechanism.