Self-supervised models create representation spaces that lack clear semantic
meaning. This interpretability problem of representations makes traditional
explainability methods ineffective in this context. In this paper, we introduce
a novel method to analyze representation spaces using three key perceptual
components: color, shape, and texture. We employ selective masking of these
components to observe changes in representations, resulting in distinct
importance maps for each. In scenarios, where labels are absent, these
importance maps provide more intuitive explanations as they are integral to the
human visual system. Our approach enhances the interpretability of the
representation space, offering explanations that resonate with human visual
perception. We analyze how different training objectives create distinct
representation spaces using perceptual components. Additionally, we examine the
representation of images across diverse image domains, providing insights into
the role of these components in different contexts.

通过使用三个关键的感知组件：颜色、形状和纹理，我们采用选择性的掩模来观察表示的变化，从而获得每个感知组件的独特重要性图，从而增强了表示空间的可解释性，提供与人类视觉感知共鸣的解释。