  Prototypical parts networks combine the power of deep learning with the
explainability of case-based reasoning to make accurate, interpretable
decisions. They follow the this looks like that reasoning, representing each
prototypical part with patches from training images. However, a single image
patch comprises multiple visual features, such as color, shape, and texture,
making it difficult for users to identify which feature is important to the
model.
  To reduce this ambiguity, we introduce the Lucid Prototypical Parts Network
(LucidPPN), a novel prototypical parts network that separates color prototypes
from other visual features. Our method employs two reasoning branches: one for
non-color visual features, processing grayscale images, and another focusing
solely on color information. This separation allows us to clarify whether the
model's decisions are based on color, shape, or texture. Additionally, LucidPPN
identifies prototypical parts corresponding to semantic parts of classified
objects, making comparisons between data classes more intuitive, e.g., when two
bird species might differ primarily in belly color.
  Our experiments demonstrate that the two branches are complementary and
together achieve results comparable to baseline methods. More importantly,
LucidPPN generates less ambiguous prototypical parts, enhancing user
understanding.
