论文标题
多样性增强了大量领域的领域概括的学习
Diversity Boosted Learning for Domain Generalization with Large Number of Domains
论文作者
论文摘要
机器学习算法使平均训练损失最小化通常由于训练数据之间相关性的贪婪开发而造成的概括性能差，而训练数据中的相关性在分配变化下并不稳定。它启发了各种域泛化作品（DG），其中一系列方法（例如因果匹配和鱼类）通过成对域操作来工作。他们需要$ o（n^2）$成对域操作，其中$ n $域通常都很昂贵。此外，虽然DG文献中的一个共同目标是学习针对域引起的虚假相关性的不变表示，但我们强调了减轻对象引起的伪造相关性的重要性。基于观察到多样性有助于减轻虚假相关性的观察，我们提出了利用确定点过程（DPP）的多样性提高了两级抽样框架（DOMI），以有效地在大量域中进行最有用的信息。我们表明，DOMI有助于训练强大的模型，以防止域侧和对象端的虚假相关性，从而大大提高了旋转的MNIST，旋转的时尚MNIST和IWILDCAM数据集对主链DG算法的性能。
Machine learning algorithms minimizing the average training loss usually suffer from poor generalization performance due to the greedy exploitation of correlations among the training data, which are not stable under distributional shifts. It inspires various works for domain generalization (DG), where a series of methods, such as Causal Matching and FISH, work by pairwise domain operations. They would need $O(n^2)$ pairwise domain operations with $n$ domains, where each one is often highly expensive. Moreover, while a common objective in the DG literature is to learn invariant representations against domain-induced spurious correlations, we highlight the importance of mitigating spurious correlations caused by objects. Based on the observation that diversity helps mitigate spurious correlations, we propose a Diversity boosted twO-level saMplIng framework (DOMI) utilizing Determinantal Point Processes (DPPs) to efficiently sample the most informative ones among large number of domains. We show that DOMI helps train robust models against spurious correlations from both domain-side and object-side, substantially enhancing the performance of the backbone DG algorithms on rotated MNIST, rotated Fashion MNIST, and iwildcam datasets.
