Deploying deep visual models can lead to performance drops due to the discrepancies between source and target distributions. Several approaches leverage labeled source data to estimate target domain accuracy, but accessing labeled source data is often prohibitively difficult due to data confidentiality or resource limitations on serving devices. Our work proposes a new framework to estimate model accuracy on unlabeled target data without access to source data. We investigate the feasibility of using pseudo-labels for accuracy estimation and evolve this idea into adopting recent advances in source-free domain adaptation algorithms. Our approach measures the disagreement rate between the source hypothesis and the target pseudo-labeling function, adapted from the source hypothesis. We mitigate the impact of erroneous pseudo-labels that may arise due to a high ideal joint hypothesis risk by employing adaptive adversarial perturbation on the input of the target model. Our proposed source-free framework effectively addresses the challenging distribution shift scenarios and outperforms existing methods requiring source data and labels for training.

翻译：在目标域与源域分布差异较大的情况下，部署深度视觉模型可能导致性能下降。现有方法通常依赖带标签的源数据来估计目标域精度，但由于数据保密性或设备资源限制，获取带标签源数据往往极为困难。本文提出一种无需源数据即可在无标签目标域上估计模型精度的新框架。我们首先验证了使用伪标签进行精度估计的可行性，进而将该思想与近期源域自适应算法的进展相结合。该方法通过测量源假设与经源假设适配后的目标伪标签函数之间的不一致率来估计精度。针对因理想联合假设风险过高导致伪标签错误的问题，我们在目标模型输入上引入自适应对抗扰动以缓解影响。所提出的无源框架有效应对了复杂的分布偏移场景，其性能优于需依赖源数据及标签进行训练的传统方法。