Most existing domain adaptation methods focus on adaptation from only one
source domain, however, in practice there are a number of relevant sources that
could be leveraged to help improve performance on target domain. We propose a
novel approach named T-SVDNet to address the task of Multi-source Domain
Adaptation (MDA), which is featured by incorporating Tensor Singular Value
Decomposition (T-SVD) into a neural network's training pipeline. Overall,
high-order correlations among multiple domains and categories are fully
explored so as to better bridge the domain gap. Specifically, we impose
Tensor-Low-Rank (TLR) constraint on a tensor obtained by stacking up a group of
prototypical similarity matrices, aiming at capturing consistent data structure
across different domains. Furthermore, to avoid negative transfer brought by
noisy source data, we propose a novel uncertainty-aware weighting strategy to
adaptively assign weights to different source domains and samples based on the
result of uncertainty estimation. Extensive experiments conducted on public
benchmarks demonstrate the superiority of our model in addressing the task of
MDA compared to state-of-the-art methods.

该研究提出了一种名为 T-SVDNet 的新方法，通过将 Tensor 奇异值分解（T-SVD）结合到神经网络的训练管道中，全面探索多个域和类别之间的高阶相关性，旨在更好地弥合域差距，实现多源域自适应。与其他方法相比，在公共基准测试上，该模型在解决 MDA 任务时表现卓越。