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.

中文翻译：

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T-SVDNet：探索多源域适应的高阶原型相关性

大多数现有的域适应方法只关注一个源域的适应，然而，在实践中，有许多相关的源可以用来帮助提高目标域的性能。我们提出了一种名为 T-SVDNet 的新方法来解决多源域适应 (MDA) 的任务，其特点是将张量奇异值分解 (T-SVD) 合并到神经网络的训练管道中。总体而言，充分探索了多个领域和类别之间的高阶相关性，以更好地弥合领域差距。具体来说，我们对通过堆叠一组原型相似性矩阵获得的张量施加 Tensor-Low-Rank (TLR) 约束，旨在捕获跨不同域的一致数据结构。此外，为了避免噪声源数据带来的负迁移，我们提出了一种新的不确定性感知加权策略，根据不确定性估计的结果自适应地为不同的源域和样本分配权重。在公共基准上进行的大量实验证明，与最先进的方法相比，我们的模型在解决 MDA 任务方面的优越性。