论文标题
不要错过不匹配：研究目标函数不匹配的无监督代表性学习
Don't miss the Mismatch: Investigating the Objective Function Mismatch for Unsupervised Representation Learning
论文作者
论文摘要
寻找无监督的表示学习技术的一般评估指标是一个具有挑战性的开放研究问题，由于对无监督方法的兴趣日益增加，最近它变得越来越必要。即使这些方法有益于有益的表示特征，但目前大多数方法都遭受了目标函数不匹配。这一不匹配指出，当学习不监督的借口任务太长时，所需目标任务的性能可能会降低 - 尤其是当两个任务都犯错时。在这项工作中，我们建立在广泛使用的线性评估协议的基础上，并定义了新的一般评估指标，以定量捕获目标函数不匹配和更通用的指标不匹配。我们以各种借口和目标任务讨论了协议的可用性和稳定性，并在各种实验中研究了不匹配。因此，我们透露了有关借口模型的大小，目标模型复杂性，借口和目标增强以及借口和目标任务类型的借口大小，目标模型的复杂性，借口和目标的依赖性不匹配的依赖性。在我们的实验中，我们发现，在许多设置中，CIFAR10，CIFAR100和PCAM的目标函数不匹配降低了〜0.1-5.0％，而3D形型数据集的极端情况下，性能不匹配。
Finding general evaluation metrics for unsupervised representation learning techniques is a challenging open research question, which recently has become more and more necessary due to the increasing interest in unsupervised methods. Even though these methods promise beneficial representation characteristics, most approaches currently suffer from the objective function mismatch. This mismatch states that the performance on a desired target task can decrease when the unsupervised pretext task is learned too long - especially when both tasks are ill-posed. In this work, we build upon the widely used linear evaluation protocol and define new general evaluation metrics to quantitatively capture the objective function mismatch and the more generic metrics mismatch. We discuss the usability and stability of our protocols on a variety of pretext and target tasks and study mismatches in a wide range of experiments. Thereby we disclose dependencies of the objective function mismatch across several pretext and target tasks with respect to the pretext model's representation size, target model complexity, pretext and target augmentations as well as pretext and target task types. In our experiments, we find that the objective function mismatch reduces performance by ~0.1-5.0% for Cifar10, Cifar100 and PCam in many setups, and up to ~25-59% in extreme cases for the 3dshapes dataset.
