  Accurately estimating model performance poses a significant challenge,
particularly in scenarios where the source and target domains follow different
data distributions. Most existing performance prediction methods heavily rely
on the source data in their estimation process, limiting their applicability in
a more realistic setting where only the trained model is accessible. The few
methods that do not require source data exhibit considerably inferior
performance. In this work, we propose a source-free approach centred on
uncertainty-based estimation, using a generative model for calibration in the
absence of source data. We establish connections between our approach for
unsupervised calibration and temperature scaling. We then employ a
gradient-based strategy to evaluate the correctness of the calibrated
predictions. Our experiments on benchmark object recognition datasets reveal
that existing source-based methods fall short with limited source sample
availability. Furthermore, our approach significantly outperforms the current
state-of-the-art source-free and source-based methods, affirming its
effectiveness in domain-invariant performance estimation.
