论文标题
代表学习的经验评估和理论分析：调查
Empirical Evaluation and Theoretical Analysis for Representation Learning: A Survey
论文作者
论文摘要
表示学习使我们能够从数据集中自动提取通用功能表示形式，以求解另一个机器学习任务。最近，通过表示学习算法和简单的预测指标提取的功能表示形式已在几个机器学习任务上表现出最先进的性能。尽管取得了显着的进展，但由于代表学习的灵活性，有多种方法可以根据应用来评估表示算法。为了了解当前的表示学习，我们回顾了表示学习算法和理论分析的评估方法。根据我们的评估调查，我们还讨论了表示学习的未来方向。请注意，这项调查是Nozawa和Sato（2022）的扩展版。
Representation learning enables us to automatically extract generic feature representations from a dataset to solve another machine learning task. Recently, extracted feature representations by a representation learning algorithm and a simple predictor have exhibited state-of-the-art performance on several machine learning tasks. Despite its remarkable progress, there exist various ways to evaluate representation learning algorithms depending on the application because of the flexibility of representation learning. To understand the current representation learning, we review evaluation methods of representation learning algorithms and theoretical analyses. On the basis of our evaluation survey, we also discuss the future direction of representation learning. Note that this survey is the extended version of Nozawa and Sato (2022).
