title: Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations
    for Vision Foundation Models
summary: Vision transformers (ViTs) have emerged as a significant area of focus,
  particularly for their capacity to be jointly trained with large language
  models and to serve as robust vision foundation models. Yet, the development of
  trustworthy explanation methods for ViTs has lagged, particularly in the
  context of post-hoc interpretations of ViT predictions. Existing sub-image
  selection approaches, such as feature-attribution and conceptual models, fall
  short in this regard. This paper proposes five desiderata for explaining ViTs
  -- faithfulness, stability, sparsity, multi-level structure, and parsimony --
  and demonstrates the inadequacy of current methods in meeting these criteria
  comprehensively. We introduce a variational Bayesian explanation framework,
  dubbed ProbAbilistic Concept Explainers (PACE), which models the distributions
  of patch embeddings to provide trustworthy post-hoc conceptual explanations.
  Our qualitative analysis reveals the distributions of patch-level concepts,
  elucidating the effectiveness of ViTs by modeling the joint distribution of
  patch embeddings and ViT's predictions. Moreover, these patch-level
  explanations bridge the gap between image-level and dataset-level explanations,
  thus completing the multi-level structure of PACE. Through extensive
  experiments on both synthetic and real-world datasets, we demonstrate that PACE
  surpasses state-of-the-art methods in terms of the defined desiderata.
paper_authors._id: 6673b896d7b401d8e4159520
paper_authors.name: Hengyi Wang
paper_authors.status: extracted_confirmed
paper_authors.statusLastChangedAt: 2024-06-20T05:05:44.009Z
paper_authors._id: 658de969a33889b27f1da90a
paper_authors.avatarUrl: /avatars/12da03b995742415ab446ff8d5b0439e.svg
paper_authors.fullname: Hengyi Wang
paper_authors.type: user
paper_authors.user: aaronwhy
paper_authors._id: 6673b896d7b401d8e4159521
paper_authors.name: Shiwei Tan
paper_authors.status: admin_assigned
paper_authors.statusLastChangedAt: 2024-06-20T07:41:14.174Z
paper_authors._id: 630d80d7c169245d7808c242
paper_authors.avatarUrl: /avatars/a6441b90ba67373bc49a34a101a29722.svg
paper_authors.fullname: Shiwei Tan
paper_authors.type: user
paper_authors.user: SKFE
paper_authors._id: 6673b896d7b401d8e4159522
paper_authors.name: Hao Wang
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-06-20T07:27:21.818Z
paper_authors._id: 630ebfa278be96577258b67a
paper_authors.avatarUrl: /avatars/dbff77354763bd934639aa3740e62ef1.svg
paper_authors.fullname: Hao Wang
paper_authors.type: user
paper_authors.user: js05212
paper_title: Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations
    for Vision Foundation Models
paper_summary: Vision transformers (ViTs) have emerged as a significant area of focus,
  particularly for their capacity to be jointly trained with large language
  models and to serve as robust vision foundation models. Yet, the development of
  trustworthy explanation methods for ViTs has lagged, particularly in the
  context of post-hoc interpretations of ViT predictions. Existing sub-image
  selection approaches, such as feature-attribution and conceptual models, fall
  short in this regard. This paper proposes five desiderata for explaining ViTs
  -- faithfulness, stability, sparsity, multi-level structure, and parsimony --
  and demonstrates the inadequacy of current methods in meeting these criteria
  comprehensively. We introduce a variational Bayesian explanation framework,
  dubbed ProbAbilistic Concept Explainers (PACE), which models the distributions
  of patch embeddings to provide trustworthy post-hoc conceptual explanations.
  Our qualitative analysis reveals the distributions of patch-level concepts,
  elucidating the effectiveness of ViTs by modeling the joint distribution of
  patch embeddings and ViT's predictions. Moreover, these patch-level
  explanations bridge the gap between image-level and dataset-level explanations,
  thus completing the multi-level structure of PACE. Through extensive
  experiments on both synthetic and real-world datasets, we demonstrate that PACE
  surpasses state-of-the-art methods in terms of the defined desiderata.
paper_ai_keywords: Vision transformers (ViTs) feature-attribution conceptual models faithfulness stability sparsity multi-level structure parsimony variational Bayesian explanation framework Probabilistic Concept Explainers (PACE) patch embeddings post-hoc conceptual explanations patch-level concepts image-level explanations dataset-level explanations state-of-the-art methods synthetic datasets real-world datasets