论文标题
通过因果机制转移的几个射击域的适应
Few-shot Domain Adaptation by Causal Mechanism Transfer
论文作者
论文摘要
我们在回归问题上研究了很少的监督域适应性（DA），其中只有少数标记的目标域数据和许多标记的源域数据。当前的许多DA方法将其转移假设以参数化的分布移位或表观分布相似性为基础，例如相同的条件或较小的分布差异。但是，这些假设可能排除从错综复杂的转移和显然非常不同的分布中适应的可能性。为了克服这个问题，我们提出了机制转移，这是一种元分布的情况，其中数据生成机制在域之间是不变的。这种转移假设可以适应非参数偏移，从而导致显然不同的分布，同时为DA提供稳固的统计基础。我们以因果建模中的结构方程为例，并提出了一种新颖的DA方法，该方法在理论上和实验上都很有用。我们的方法可以看作是第一次完全利用DA的结构因果模型的尝试。
We study few-shot supervised domain adaptation (DA) for regression problems, where only a few labeled target domain data and many labeled source domain data are available. Many of the current DA methods base their transfer assumptions on either parametrized distribution shift or apparent distribution similarities, e.g., identical conditionals or small distributional discrepancies. However, these assumptions may preclude the possibility of adaptation from intricately shifted and apparently very different distributions. To overcome this problem, we propose mechanism transfer, a meta-distributional scenario in which a data generating mechanism is invariant among domains. This transfer assumption can accommodate nonparametric shifts resulting in apparently different distributions while providing a solid statistical basis for DA. We take the structural equations in causal modeling as an example and propose a novel DA method, which is shown to be useful both theoretically and experimentally. Our method can be seen as the first attempt to fully leverage the structural causal models for DA.
