Rethinking Calibration for Early-Exit Neural Networks
We challenge the use of calibration metrics in early-exit models and show cases where calibration fails to accurately reflect the network performance. We argue for failure prediction as a more reliable performance proxy that better correlates with efficiency gains in early-exit networks.
Jul 1, 2026