论文标题
优化的通用功能学习，用于跨域的几次分类
Optimized Generic Feature Learning for Few-shot Classification across Domains
论文作者
论文摘要
学习跨任务和域的模型或功能是机器学习的宏伟目标之一。在本文中，我们建议将跨域的跨任务数据作为超参数优化（HPO）的验证目标，以改善此目标。鉴于足够丰富的搜索空间，优化超参数的学习功能可以最大程度地提高验证性能，并且由于目标，因此跨任务和域进行了概括。我们证明了该策略在范围内和跨域内的几个图像分类中的有效性。学识渊博的功能的表现优于所有前几次和元学习方法。
To learn models or features that generalize across tasks and domains is one of the grand goals of machine learning. In this paper, we propose to use cross-domain, cross-task data as validation objective for hyper-parameter optimization (HPO) to improve on this goal. Given a rich enough search space, optimization of hyper-parameters learn features that maximize validation performance and, due to the objective, generalize across tasks and domains. We demonstrate the effectiveness of this strategy on few-shot image classification within and across domains. The learned features outperform all previous few-shot and meta-learning approaches.
