A Study of Unsupervised Evaluation Metrics for Practical and Automatic Domain Adaptation

Minghao Chen, zepeng gao, Shuai Zhao, Qibo Qiu, Ke Li, Binbin Lin, Xiaofei He

Primary Area: transfer learning, meta learning, and lifelong learning

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Keywords: Unsupervised Evaluation Metrics, Unsupervised Domain Adaptation

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Abstract: Unsupervised domain adaptation (UDA) methods facilitate the transfer of models to target domains without labels. However, these methods necessitate a labeled target validation set for hyper-parameter tuning and model selection. In this paper, we aim to find an evaluation metric capable of assessing the quality of a transferred model without access to target validation labels. We begin with the metric based on mutual information of the model prediction. Through empirical analysis, we identify three prevalent issues with this metric: 1) It does not account for the source structure. 2) It can be easily attacked. 3) It fails to detect negative transfer caused by the over-alignment of source and target features. To address the first two issues, we incorporate source accuracy into the metric and employ a new MLP classifier that is held out during training, significantly improving the result. To tackle the final issue, we integrate this enhanced metric with data augmentation, resulting in a novel unsupervised UDA metric called the Augmentation Consistency Metric (ACM). Additionally, we empirically demonstrate the shortcomings of previous experiment settings and conduct large-scale experiments to validate the effectiveness of our proposed metric. Furthermore, we leverage our metric to automatically search for the optimal set of hyper-parameters, achieving superior performance comparable to manually tuned sets across four common benchmarks.

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Submission Number: 900