论文标题
转移学习的限制
Limits of Transfer Learning
论文作者
论文摘要
转移学习涉及从一个问题域中获取信息和洞察力，并将其应用于新的问题领域。尽管在实践中广泛使用，但转移学习的理论仍然不那么发达。为了解决这个问题，我们证明了与转移学习有关的几个新颖结果，表明有必要仔细选择要转移的信息集以及传输信息和目标问题之间依赖性的需求。此外，我们证明了使用转移学习的算法中概率变化的程度如何在可能的改进量上具有上限。这些结果基于用于机器学习的算法搜索框架，从而使结果适用于使用转移的广泛学习问题。
Transfer learning involves taking information and insight from one problem domain and applying it to a new problem domain. Although widely used in practice, theory for transfer learning remains less well-developed. To address this, we prove several novel results related to transfer learning, showing the need to carefully select which sets of information to transfer and the need for dependence between transferred information and target problems. Furthermore, we prove how the degree of probabilistic change in an algorithm using transfer learning places an upper bound on the amount of improvement possible. These results build on the algorithmic search framework for machine learning, allowing the results to apply to a wide range of learning problems using transfer.
