  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
