Papers › Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling
Pick up the PACE: Fast and Simple Domain Adaptation via Ensemble Pseudo-Labeling
Christopher Liao, Theodoros Tsiligkaridis, Brian Kulis
Domain Adaptation (DA) has received widespread attention from deep learning researchers in recent years because of its potential to improve test accuracy with out-of-distribution labeled data. Most state-of-the-art DA algorithms require an extensive amount of hyperparameter tuning and are computationally intensive due to the large batch sizes required. In this work, we propose a fast and simple DA method consisting of three stages: (1) domain alignment by covariance matching, (2) pseudo-labeling, and (3) ensembling. We call this method PACE, for Pseudo-labels, Alignment of Covariances, and Ensembles. PACE is trained on top of fixed features extracted from an ensemble of modern pretrained backbones. PACE exceeds previous state-of-the-art by 5 - 10 % on most benchmark adaptation tasks without training a neural network. PACE reduces training time and hyperparameter tuning time by 82% and 97%, respectively, when compared to state-of-the-art DA methods. Code is released here: https://github.com/Chris210634/PACE-Domain-Adaptation
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