  For debugging and verification of computer vision convolutional deep neural
networks (CNNs) human inspection of the learned latent representations is
imperative. Therefore, state-of-the-art eXplainable Artificial Intelligence
(XAI) methods globally associate given natural language semantic concepts with
representing vectors or regions in the CNN latent space supporting manual
inspection. Yet, this approach comes with two major disadvantages: They are
locally inaccurate when reconstructing a concept label and discard information
about the distribution of concept instance representations. The latter, though,
is of particular interest for debugging, like finding and understanding
outliers, learned notions of sub-concepts, and concept confusion. Furthermore,
current single-layer approaches neglect that information about a concept may be
spread over the CNN depth. To overcome these shortcomings, we introduce the
local-to-global Guided Concept Projection Vectors (GCPV) approach: It (1)
generates local concept vectors that each precisely reconstruct a concept
segmentation label, and then (2) generalizes these to global concept and even
sub-concept vectors by means of hiearchical clustering. Our experiments on
object detectors demonstrate improved performance compared to the
state-of-the-art, the benefit of multi-layer concept vectors, and robustness
against low-quality concept segmentation labels. Finally, we demonstrate that
GCPVs can be applied to find root causes for confusion of concepts like bus and
truck, and reveal interesting concept-level outliers. Thus, GCPVs pose a
promising step towards interpretable model debugging and informed data
improvement.
