Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations
  for Vision Foundation Models
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.
6673b896d7b401d8e4159520
Hengyi Wang
extracted_confirmed
2024-06-20T05:05:44.009Z
658de969a33889b27f1da90a
/avatars/12da03b995742415ab446ff8d5b0439e.svg
Hengyi Wang
user
aaronwhy
6673b896d7b401d8e4159521
Shiwei Tan
admin_assigned
2024-06-20T07:41:14.174Z
630d80d7c169245d7808c242
/avatars/a6441b90ba67373bc49a34a101a29722.svg
Shiwei Tan
user
SKFE
6673b896d7b401d8e4159522
Hao Wang
claimed_verified
2024-06-20T07:27:21.818Z
630ebfa278be96577258b67a
/avatars/dbff77354763bd934639aa3740e62ef1.svg
Hao Wang
user
js05212
Probabilistic Conceptual Explainers: Trustworthy Conceptual Explanations
  for Vision Foundation Models
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.
A probabilistic explanation framework, PACE, enhances the interpretability of vision transformers, addressing shortcomings in existing methods through a variational Bayesian approach.
vision transformers sub-image selection feature-attribution conceptual models faithfulness stability sparsity multi-level structure parsimony variational Bayesian ProbAbilistic Concept Explainers (PACE) patch embeddings patch-level concepts image-level explanations dataset-level explanations