Active domain adaptation (DA) aims to maximally boost the model adaptation on a new target domain by actively selecting limited target data to annotate, whereas traditional active learning methods may be less effective since they do not consider the domain shift issue. Despite active DA methods address this by further proposing targetness to measure the representativeness of target domain characteristics, their predictive uncertainty is usually based on the prediction of deterministic models, which can easily be miscalibrated on data with distribution shift. Considering this, we propose a \textit{Dirichlet-based Uncertainty Calibration} (DUC) approach for active DA, which simultaneously achieves the mitigation of miscalibration and the selection of informative target samples. Specifically, we place a Dirichlet prior on the prediction and interpret the prediction as a distribution on the probability simplex, rather than a point estimate like deterministic models. This manner enables us to consider all possible predictions, mitigating the miscalibration of unilateral prediction. Then a two-round selection strategy based on different uncertainty origins is designed to select target samples that are both representative of target domain and conducive to discriminability. Extensive experiments on cross-domain image classification and semantic segmentation validate the superiority of DUC.
翻译:主动域适应(DA)的目的是通过积极选择有限的目标数据,在新的目标领域上最大限度地促进模型适应,办法是积极为注释性选择有限的目标数据,而传统的主动学习方法可能不那么有效,因为它们不考虑领域转移问题。尽管主动的DA方法解决这一问题,进一步提出衡量目标域特性代表性的目标,但它们的预测不确定性通常基于确定模型的预测,这种模型很容易在分布变化时对数据进行错误校准。考虑到这一点,我们提议对活跃的DA采取\ textit{Drichlet-基于不确定性校准} (DUC) 方法,这种办法可以同时减少误差和选择信息性目标样本。具体地说,我们在预测之前设置了Drichlet,并将预测解释为概率简单值的分布,而不是确定性模型等点估计值。这种方式使我们能够考虑所有可能的预测,减轻单方面预测的误差。然后根据不同的不确定性来源设计一个两轮选择战略,以选择目标样品,既代表目标域域,又有助于确定性图像的可辨别性。</s>