In this genre of model, a classifier is directly trained to predict whether the current internal classifier has made the correct prediction. 

New York University

Early exit method aims to terminate the computation in earlier layer to reduce computation and achieve a better performance. The key of such method is the exit criterion, which can be categorized as: confidence-based, ensemble-based and learning-based.

Early Exit

Xu, C., & McAuley, J. (2022). A Survey on Dynamic Neural Networks for Natural Language Processing. ArXiv, abs/2202.07101.

A Survey on Dynamic Neural Networks for Natural Language Processing

The benefits of early exit model include better performance, higher computational speed, and adversarial robustness. In addition, the speed-accuracy trade-off can be adjusted by tuning the exit threshold during inference without retraining the model.
The drawback comes along is that the batch size is usually 1 since early exist is applied per-instance. 


Benefit and Drawback of Early Exit

In confidence-based early exit model, a indicator of confidence of the prediction is defined, and when this indicator hits the threshold, the model will exits. 

Confidence-Based Early Exit

One draw back of confidence-based early exit is wasted computation when the confidence indicator does not reach the threshold. On the contrary, ensemble-based early exit model makes use of predictions of multiple internal classifier and makes better prediction. 

Ensemble-Based Early Exit

Learning-Based Early Exit

Cascading is a special type of early exit at model level instead of layer level. For example, CascadeBERT executes a sequence of models one by one, and it exits when one of them outputs a score that reaches the threshold.

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