The advances in deep learning have enabled machine learning methods to outperform human beings in various areas, but it remains a great challenge for a well-trained model to quickly adapt to a new task. One promising solution to realize this goal is through meta-learning, also known as learning to learn, which has achieved promising results in few-shot learning. However, current approaches are still enormously different from human beings' learning process, especially in the ability to extract structural and transferable knowledge. This drawback makes current meta-learning frameworks non-interpretable and hard to extend to more complex tasks. We tackle this problem by introducing concept discovery to the few-shot learning problem, where we achieve more effective adaptation by meta-learning the structure among the data features, leading to a composite representation of the data. Our proposed method Concept-Based Model-Agnostic Meta-Learning (COMAML) has been shown to achieve consistent improvements in the structured data for both synthesized datasets and real-world datasets.
翻译:深层次学习的进步使得机器学习方法能够在各个领域优于人,但对于训练有素的模式来说,要迅速适应新的任务,这仍然是一个巨大的挑战。实现这一目标的一个大有希望的解决办法是通过元学习,也称为学习学习,在微小的学习中取得了有希望的成果。然而,目前的方法仍然与人类的学习过程大不相同,特别是在提取结构和可转让知识的能力方面。这一缺陷使得目前的元学习框架无法解释,难以扩展到更复杂的任务。我们通过将概念发现引入微小的学习问题来解决这一问题,我们通过对数据特征的结构进行元学习,从而更有效地适应数据特征的结构,从而形成数据的综合表述。我们提议的基于概念的模型-遗传元学习方法(COMAML)已经表明,在综合数据集和真实世界数据集的结构性数据方面都实现了一致的改进。