通过复杂性视角理解视觉特征依赖

Understanding Visual Feature Reliance through the Lens of Complexity

July 8, 2024

作者: Thomas Fel, Louis Bethune, Andrew Kyle Lampinen, Thomas Serre, Katherine Hermann

cs.AI

摘要

最近的研究表明，深度学习模型对倾向于偏爱简单特征的归纳偏差可能是捷径学习的一个来源之一。然而，对于模型学习的众多特征的复杂性了解仍然有限。在这项工作中，我们引入了一个新的度量标准来量化特征的复杂性，基于V-信息并捕捉特征是否需要复杂的计算转换才能被提取出来。利用这个V-信息度量标准，我们分析了从一个标准的ImageNet训练的视觉模型中提取的10,000个特征的复杂性，这些特征被表示为倒数第二层中的方向。我们的研究涉及四个关键问题：首先，我们探讨了特征在复杂性方面的表现，并发现模型中存在简单到复杂的特征光谱。其次，我们研究了特征在训练过程中何时被学习。我们发现，在训练初期简单特征占主导地位，而更复杂的特征逐渐出现。第三，我们调查了网络内简单和复杂特征流动的位置，并发现简单特征倾向于通过残差连接绕过视觉层次结构。第四，我们探讨了特征复杂性与它们在驱动网络决策中的重要性之间的联系。我们发现复杂特征往往不那么重要。令人惊讶的是，重要特征在训练过程中更早地变得可访问，就像沉淀过程一样，使模型能够建立在这些基础元素之上。

English

Recent studies suggest that deep learning models inductive bias towards favoring simpler features may be one of the sources of shortcut learning. Yet, there has been limited focus on understanding the complexity of the myriad features that models learn. In this work, we introduce a new metric for quantifying feature complexity, based on V-information and capturing whether a feature requires complex computational transformations to be extracted. Using this V-information metric, we analyze the complexities of 10,000 features, represented as directions in the penultimate layer, that were extracted from a standard ImageNet-trained vision model. Our study addresses four key questions: First, we ask what features look like as a function of complexity and find a spectrum of simple to complex features present within the model. Second, we ask when features are learned during training. We find that simpler features dominate early in training, and more complex features emerge gradually. Third, we investigate where within the network simple and complex features flow, and find that simpler features tend to bypass the visual hierarchy via residual connections. Fourth, we explore the connection between features complexity and their importance in driving the networks decision. We find that complex features tend to be less important. Surprisingly, important features become accessible at earlier layers during training, like a sedimentation process, allowing the model to build upon these foundational elements.