  Negative flips are errors introduced in a classification system when a legacy
model is updated. Existing methods to reduce the negative flip rate (NFR)
either do so at the expense of overall accuracy by forcing a new model to
imitate the old models, or use ensembles, which multiply inference cost
prohibitively. We analyze the role of ensembles in reducing NFR and observe
that they remove negative flips that are typically not close to the decision
boundary, but often exhibit large deviations in the distance among their
logits. Based on the observation, we present a method, called Ensemble Logit
Difference Inhibition (ELODI), to train a classification system that achieves
paragon performance in both error rate and NFR, at the inference cost of a
single model. The method distills a homogeneous ensemble to a single student
model which is used to update the classification system. ELODI also introduces
a generalized distillation objective, Logit Difference Inhibition (LDI), which
only penalizes the logit difference of a subset of classes with the highest
logit values. On multiple image classification benchmarks, model updates with
ELODI demonstrate superior accuracy retention and NFR reduction.
