Failure Prediction Is a Better Performance Proxy for Early-Exit Networks Than Calibration

• URL: http://arxiv.org/abs/2508.21495v2
• Date: Sat, 06 Sep 2025 18:29:33 GMT
• Title: Failure Prediction Is a Better Performance Proxy for Early-Exit Networks Than Calibration
• Abstract summary: We argue that calibration metrics can be misleading indicators of multi-exit model performance.<n>We propose using failure prediction as a more informative proxy for early-exit model performance.
• Score: 4.73982447569174
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Early-exit models accelerate inference by attaching internal classifiers to intermediate layers of the network, allowing computation to halt once a prediction meets a predefined exit criterion. Most early-exit methods rely on confidence-based exit strategies, which has motivated prior work to calibrate intermediate classifiers in pursuit of improved performance-efficiency trade-offs. In this paper, we argue that calibration metrics can be misleading indicators of multi-exit model performance. Specifically, we present empirical evidence showing that miscalibrated networks can outperform calibrated ones. As an alternative, we propose using failure prediction as a more informative proxy for early-exit model performance. Unlike calibration, failure prediction captures changes in sample rankings and correlates strongly with efficiency gains, offering a more reliable framework for designing and evaluating early-exit models.

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