This work introduces the novel task of Source-free Multi-target Domain Adaptation and proposes adaptation framework comprising of \textbf{Co}nsistency with \textbf{N}uclear-Norm Maximization and \textbf{Mix}Up knowledge distillation (\textit{CoNMix}) as a solution to this problem. The main motive of this work is to solve for Single and Multi target Domain Adaptation (SMTDA) for the source-free paradigm, which enforces a constraint where the labeled source data is not available during target adaptation due to various privacy-related restrictions on data sharing. The source-free approach leverages target pseudo labels, which can be noisy, to improve the target adaptation. We introduce consistency between label preserving augmentations and utilize pseudo label refinement methods to reduce noisy pseudo labels. Further, we propose novel MixUp Knowledge Distillation (MKD) for better generalization on multiple target domains using various source-free STDA models. We also show that the Vision Transformer (VT) backbone gives better feature representation with improved domain transferability and class discriminability. Our proposed framework achieves the state-of-the-art (SOTA) results in various paradigms of source-free STDA and MTDA settings on popular domain adaptation datasets like Office-Home, Office-Caltech, and DomainNet. Project Page: https://sites.google.com/view/conmix-vcl

该研究提出了Source-free Multi-target Domain Adaptation的新任务，并采用由Consistency with Nuclear-Norm Maximization和MixUp knowledge distillation（CoNMix）组成的适应性框架作为解决此问题的方法。该框架利用目标伪标签改善目标适应性，并使用伪标签细化方法减少噪声伪标签。此外，我们还提出了MixUp Knowledge Distillation（MKD）用于改进多个目标域的泛化能力，并展示了Vision Transformer（VT）骨干具有更好的特征表示和分类可辨别性。最终，该框架在多种源自由STDA和MTDA设置的流行域适应数据集上实现了最先进的成果。

基于CoNMix的无源单目标和多目标域自适应