  Recent advancements in post-hoc and inherently interpretable methods have
markedly enhanced the explanations of black box classifier models. These
methods operate either through post-analysis or by integrating concept learning
during model training. Although being effective in bridging the semantic gap
between a model's latent space and human interpretation, these explanation
methods only partially reveal the model's decision-making process. The outcome
is typically limited to high-level semantics derived from the last feature map.
We argue that the explanations lacking insights into the decision processes at
low and mid-level features are neither fully faithful nor useful. Addressing
this gap, we introduce the Multi-Level Concept Prototypes Classifier (MCPNet),
an inherently interpretable model. MCPNet autonomously learns meaningful
concept prototypes across multiple feature map levels using Centered Kernel
Alignment (CKA) loss and an energy-based weighted PCA mechanism, and it does so
without reliance on predefined concept labels. Further, we propose a novel
classifier paradigm that learns and aligns multi-level concept prototype
distributions for classification purposes via Class-aware Concept Distribution
(CCD) loss. Our experiments reveal that our proposed MCPNet while being
adaptable to various model architectures, offers comprehensive multi-level
explanations while maintaining classification accuracy. Additionally, its
concept distribution-based classification approach shows improved
generalization capabilities in few-shot classification scenarios.
