Addressing the rising concerns of privacy and security, domain adaptation in the dark aims to adapt a black-box source trained model to an unlabeled target domain without access to any source data or source model parameters. The need for domain adaptation of black-box predictors becomes even more pronounced to protect intellectual property as deep learning based solutions are becoming increasingly commercialized. Current methods distill noisy predictions on the target data obtained from the source model to the target model, and/or separate clean/noisy target samples before adapting using traditional noisy label learning algorithms. However, these methods do not utilize the easy-to-hard learning nature of the clean/noisy data splits. Also, none of the existing methods are end-to-end, and require a separate fine-tuning stage and an initial warmup stage. In this work, we present Curriculum Adaptation for Black-Box (CABB) which provides a curriculum guided adaptation approach to gradually train the target model, first on target data with high confidence (clean) labels, and later on target data with noisy labels. CABB utilizes Jensen-Shannon divergence as a better criterion for clean-noisy sample separation, compared to the traditional criterion of cross entropy loss. Our method utilizes co-training of a dual-branch network to suppress error accumulation resulting from confirmation bias. The proposed approach is end-to-end trainable and does not require any extra finetuning stage, unlike existing methods. Empirical results on standard domain adaptation datasets show that CABB outperforms existing state-of-the-art black-box DA models and is comparable to white-box domain adaptation models.

翻译：针对日益增长的隐私与安全担忧，暗域中的领域自适应旨在无需访问任何源数据或源模型参数的情况下，将基于源数据训练的黑盒模型适应至无标注的目标域。随着深度学习解决方案日益商业化，保护知识产权使得黑盒预测器的领域自适应需求更加突出。现有方法通过从源模型获取的目标数据噪声预测来蒸馏至目标模型，和/或在采用传统噪声标签学习算法进行自适应之前分离干净/噪声目标样本。然而，这些方法未利用干净/噪声数据分组的由易到难学习特性。此外，现有方法均非端到端，需要独立的微调阶段和初始预热阶段。本文提出黑盒课程自适应方法，通过课程引导的自适应策略逐步训练目标模型：首先使用高置信度（干净）标签的目标数据，再逐步引入噪声标签的目标数据。与传统交叉熵损失准则相比，该方法采用詹森-香农散度作为更优的干净-噪声样本分离标准。我们通过双分支网络的协同训练抑制确认偏差导致的误差累积。与现有方法不同，本方法可端到端训练且无需额外微调阶段。在标准领域自适应数据集上的实验结果表明，黑盒课程自适应方法优于现有最先进的黑盒领域适应模型，性能与白盒领域适应模型相当。