DIRICHLET-BASED UNCERTAINTY CALIBRATION FOR ACTIVE DOMAIN ADAPTATION

Abstract

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 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.

1. INTRODUCTION

Despite the superb performances of deep neural networks (DNNs) on various tasks (Krizhevsky et al., 2012; Chen et al., 2015) , their training typically requires massive annotations, which poses formidable cost for practical applications. Moreover, they commonly assume training and testing data follow the same distribution, making the model brittle to distribution shifts (Ben-David et al., 2010) . Alternatively, unsupervised domain adaptation (UDA) has been widely studied, which assists the model learning on an unlabeled target domain by transferring the knowledge from a labeled source domain (Ganin & Lempitsky, 2015; Long et al., 2018) . Despite the great advances of UDA, the unavailability of target labels greatly limits its performance, presenting a huge gap with the supervised counterpart. Actually, given an acceptable budget, a small set of target data can be annotated to significantly boost the performance of UDA. With this consideration, recent works (Fu et al., 2021; Prabhu et al., 2021) integrate the idea of active learning (AL) into DA, resulting in active DA. The core of active DA is to annotate the most valuable target samples for maximally benefiting the adaptation. However, traditional AL methods based on either predictive uncertainty or diversity are less effective for active DA, since they do not consider the domain shift. For predictive uncertainty (e.g., margin (Joshi et al., 2009) , entropy (Wang & Shang, 2014)) based methods, they cannot measure the target-representativeness of samples. As a result, the selected samples are often redundant and less informative. As for diversity based methods (Sener & Savarese, 2018; Nguyen & Smeulders, 2004) , they may select samples that are already well-aligned with source domain (Prabhu et al., 2021) . Aware of these, active DA methods integrate both predictive uncertainty and targetness into the selection process (Su et al., 2019; Fu et al., 2021; Prabhu et al., 2021 ). Yet, existing focus is on the measurement of targetness, e.g., using domain discriminator (Su et al., 2019) or clustering (Prabhu et al., 2021) . The predictive uncertainty they used is still mainly based on the prediction of deterministic models, which is essentially a point estimate (Sensoy et al., 2018) and can easily be miscalibrated on data with distribution shift (Guo et al., 2017) . As in Fig. 1 (a), standard DNN is wrongly overconfident on most target data. Correspondingly, its predictive uncertainty is unreliable.

