Sparks of Explainability: Recent Advancements in Explaining Large Vision Models

Thomas Fel

REVIEW 3 major objections 5 minor 300 references

Sparks of Explainability: Recent Advancements in Explaining Large Vision Models

T0 review · 3 major / 5 minor · reviewed 2026-08-09 · deepseek-v4-flash

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Pith's one-line read This thesis argues that attribution methods only reveal where a vision model looks, and that real explainability requires concept-level tools (CRAFT, MACO) and human-aligned training.

desk verdict A well-packaged compilation of already-published XAI methods whose central 'what vs where' claim overstates CRAFT's causal faithfulness; a useful synthesis, not new research. read the letter →

arxiv 2502.01048 v1 pith:62X2WCFG submitted 2025-02-03 cs.CV cs.AI

Thomas Fel This is my paper · ORCID

classification cs.CVcs.AI MSC 68T0768T45

keywords explainableAIattributionmethodsconcept-basedexplainabilitySobolindicesfeaturevisualization1-LipschitznetworksvisionmodelsCRAFT

verification ladder T0 review T1 audit T2 compute T3 formal

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The reading

This thesis argues that standard attribution methods—saliency maps and their relatives—answer only where a vision model looks, not what it perceives, and that this is why they prove inadequate in real-world, complex scenarios. To support this, the author develops a family of explainability tools: a stability-based metric for judging explanations, a Sobol-index attribution method that runs twice as fast as previous black-box approaches, and EVA, an attribution method with formal guarantees via verified perturbation analysis. User studies then show that even the best attribution methods offer little help beyond simple bias detection. The thesis concludes that explainability should move toward concept-based methods that extract the model's internal vocabulary (CRAFT and MACO) and toward training routines that align models with human reasoning, which also improve accuracy.

What carries the argument

The central object is the concept vector in the activation space of the network, with CRAFT applying non-negative matrix factorization of activations to obtain a dictionary of concept vectors that are then scored by their contribution to predictions. Around this sit: Sobol indices with quasi-Monte Carlo sampling for efficient black-box attribution; verified perturbation analysis for EVA's formal guarantees; and the 1-Lipschitz constraint that bounds gradients and makes explanations stable and consistent. The unification claim treats the extraction phase as dictionary learning and the importance phase as attribution applied to concepts.

What would settle it

Train a model on a dataset where the true decision rule is known and controllable, for example a clean classifier that relies on a single shape feature; if CRAFT's concept importance ranking does not put that feature first, or if removing the top concept directions from the activations leaves the model's predictions unchanged, then the concepts are not faithful.

Watch

Extended reading notes

Core claim

The central claim is that the "where" information provided by attribution methods is insufficient to understand a deep vision model: in complex scenarios, knowing which pixels a model attends to does not tell you which features or concepts trigger the decision. The thesis therefore establishes that explainability must answer "what" the model perceives, and develops CRAFT, a method that automatically extracts the concepts used by a model from its activation space and estimates their importance, together with MACO, a feature-visualization method that renders those concepts. It also shows that aligning models with human explanations—either by imitation or by constraining the model to 1-Lipschitz functions—improves both accuracy and explanation quality. A unifying analysis reveals that concept extraction is a dictionary-learning problem and that concept importance estimation is, implicitly, an attribution problem.

Load-bearing premise

The analysis assumes that the concept vectors extracted by factoring activations correspond to the features the model actually uses to make its decisions, rather than being merely low-rank directions that happen to correlate with human labels.

Editorial extensions

If this is right

If attribution methods provide only "where" information, then safety-critical deployments of explainability should not rely on heatmaps alone; concept-level explanations are needed.
Concept extraction and concept importance estimation form one coherent framework, so progress in attribution methods transfers directly to concept-based explainability.
Training with human-aligned objectives can increase both accuracy and explanation stability, suggesting that interpretability need not come at the cost of performance.
1-Lipschitz neural networks provide provably bounded gradients, which translates into more generalizable and consistent explanations.
Quasi-Monte Carlo Sobol indices yield black-box explanations that are twice as fast as RISE and competitive with white-box methods.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

If concept vectors are a faithful vocabulary of the model, the same machinery could be used for model editing: shifting activation directions to remove a concept, such as a spurious cue, and measuring the effect on behavior.
The where-versus-what distinction suggests a two-stage explanation pipeline—localize with attribution, then identify the concept at that location—which the thesis itself only partially implements through CRAFT combined with MACO.
Because the importance stage is shown to be a form of attribution, concept-based methods may inherit known attribution failure modes, such as sensitivity to baseline choice; stress-testing CRAFT's importance scores under different baselines would be a natural next experiment.
The alignment results raise the possibility that alignment with human explanations correlates with robustness; a direct test would be measuring adversarial robustness across harmonized and standard architectures.

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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Desk Editor's Note

read the letter

This is a doctoral thesis that re-presents the author's peer-reviewed XAI papers (Sobol, EVA, CRAFT, MACO, harmonization) inside a narrative arc: attribution methods tell you where a model looks, concept methods tell you what it sees, and aligning models with human reasoning improves both. As a compilation of already-reviewed work, it introduces no new equations, experiments, or methods; the novelty is in the framing and in the unified dictionary-learning view of concept extraction (Section 4.4), which was itself published at NeurIPS 2023. What the thesis does well is lay out the full stack—from black-box attribution with formal guarantees (EVA) to concept extraction and feature visualization—backed by open-source code and human experiments. That is real, reproducible evidence, and the individual methods are solid.

The soft spot is the load-bearing claim that CRAFT's concepts are the model's actual vocabulary, rather than plausible summaries. NMF on activations produces factors that correlate with human labels and pass fidelity-style tests, but the thesis does not show that the model's decision function causally depends on these factors. The human and fidelity experiments in Appendices C.1.5–C.1.6 support correlation and plausibility; they do not rule out the alternative that CRAFT re-describes the data manifold rather than the model's computation. That gap is real, and the thesis would be stronger if it framed the 'what' claim as an interpretation with supporting evidence instead of an established fact. The 'first' claims (first formal-guarantee attribution method, etc.) are also asserted without a systematic literature review; some are likely true, but the thesis does not establish priority. Chapter 3.3 is an outline rather than a full section, so the 1-Lipschitz alignment argument is underdeveloped in this arXiv version. To his credit, the author does list limitations in Appendix C.1.1, but the main text does not carry those caveats into the central argument.

Who is this for: graduate students entering XAI and researchers wanting a single entry point to this line of work. It is not a new research contribution, but it is a genuinely useful synthesis. If submitted for peer review as a survey or thesis, it deserves a serious referee; the author should be asked to soften the causal claims, substantiate the priority claims, and complete the outline chapter. I would not cite the thesis itself in my own papers—I would cite the underlying peer-reviewed articles—but I would bring it to a reading group as a case study in how a research program gets framed.

Referee Report

3 major / 5 minor

Summary. This doctoral thesis compiles a series of peer-reviewed contributions in explainable computer vision and integrates them into a single narrative. Chapter 2 develops attribution methods and evaluation tools: a stability-based metric (MeGe/ReCo), a Sobol-indices black-box attribution method accelerated with quasi-Monte Carlo sampling, the EVA method with verified perturbation guarantees, and a human-centered meta-predictor evaluation of attribution usefulness. The central negative finding is that attribution methods reveal only where a model looks, not what it perceives, and are therefore of limited use in complex scenarios. Two remedies are then explored: aligning models with human explanations, including training with human rationales and 1-Lipschitz constrained networks (Chapter 3), and concept-based explainability, concretized by CRAFT, a unified extraction/importance framework, MACO feature visualization, and an interactive demo (Chapter 4). The thesis concludes with a discussion of future directions.

Significance. If the central claim is established, the thesis would provide a well-articulated argument for shifting explainability research from pixel-level attribution to concept-level and human-aligned methods. The individual methods have already passed peer review and are benchmarked on ImageNet, Pascal VOC, COCO, FRSign, and human studies. Concrete strengths include the reproducibility orientation: code is released via Xplique, CRAFT, and project repositories, and several derivations (Sobol indices estimators, EVA's verified perturbation analysis, the dictionary-learning unification of concept extraction) are technically grounded. The thesis also makes an honest attempt to evaluate methods with actual users rather than relying only on proxy metrics. The main risk is that the most load-bearing interpretive step, namely that CRAFT's extracted concepts are the causal features underlying model decisions, is supported by correlation, human plausibility, and input-perturbation fidelity rather than by causal intervention evidence; this gap directly affects the 'what' claim that motivates the proposed shift.

major comments (3)

[§4.2 and Appendix C.1.4–C.1.6] The central 'what' claim rests on treating the non-negative matrix factorization of activation vectors as the model's internal vocabulary, with importance estimated by Sobol indices. The evidence in C.1.5 and C.1.6 demonstrates that extracted concepts align with human judgments and with input perturbations, but that is plausibility and correlation, not causal faithfulness. NMF is a low-rank reconstruction of the activation distribution, and it can recover components that correlate with human labels without being the features on which the decision function actually depends. The thesis should either add a direct intervention test, such as ablating or perturbing concept directions and measuring the resulting prediction change against a control dictionary matched for reconstruction error, or explicitly moderate the claim that CRAFT explains 'what' the model perceives.
[§2.6 and Appendix A.5] The negative conclusion that attribution methods are of little use in complex scenarios is load-bearing because it motivates both subsequent hypotheses. The manuscript states that real users were tested but does not, in the provided text, give enough detail on participant numbers, task design, statistical power, or confidence intervals to rule out the possibility that the null result is an artifact of the specific visualization or interface. For a claim of this strength, the human study should be reported with the same rigor as the other experiments, including a precise definition of what counts as 'useful' and a comparison against a baseline explanation format.
[§3.3 and Chapter 3 conclusion] The alignment hypothesis is examined in part through 1-Lipschitz networks, but Section 3.3 is only a brief outline in the provided manuscript. The conclusion that robustness improves human-aligned explanations is therefore established only for this constrained function class and depends on an optimal-transport perspective that is not presented in enough detail to verify. Either the section should be expanded to include the full derivation and experiments, or the conclusion should be narrowed so that the thesis does not imply a general alignment benefit across standard architectures.

minor comments (5)

[§1.1.2, Definition 1.1.6] The convolution definition says 'where N denotes the convolution operation,' but N is already a notation used for the set of integers and does not match the convolution symbol used elsewhere in the formula; this is a typographical or notation error that should be corrected.
[§2.1.4] The reference to 'Agarwal et al.' near the end of the stability discussion is incomplete; the authors and year should be supplied.
[§1.3] The text refers to 'Chapter Chapter 2 and Chapter Chapter 4'; the duplicated word and the awkward phrasing should be cleaned up.
[Title and Chapter 4] The title advertises 'Large Vision Models,' but the experiments are conducted on ResNet-50, VGG-16, EfficientNet, MobileNet, and ViT-S/32, which are not 'large' by current standards. The title should either be qualified or the scope should be stated more precisely.
[Throughout] Several chapters read as summaries or reprints of the underlying conference papers rather than as a fully integrated monograph; adding explicit signposts about which content is reproduced verbatim and which is new synthesis would help the reader evaluate the incremental contribution.

Circularity Check

0 steps flagged · score 1.0 of 10

No significant circularity: the thesis's central claim is supported by external human and fidelity evaluations, and no derivation reduces to its own inputs by construction.

full rationale

The thesis is a compilation of the author's own peer-reviewed papers, and it does rely heavily on self-citation, which is normal for a doctoral dissertation. However, the load-bearing claims are not justified by definitional identity or by fitted parameters renamed as predictions. The central claim that attribution methods provide only 'where' and that concept-based methods (CRAFT) provide 'what' is supported by human experiments and fidelity benchmarks that are external to the methods' definitions. The CRAFT method extracts concepts via non-negative matrix factorization of activations and estimates their importance with Sobol indices; this is a proposed tool with empirical validation, not a theorem equating its output with the model's causal vocabulary. The main scientific risk (that NMF components may correlate with human labels without being causally used by the model) is a correctness concern, not a circularity. No equation in the visible derivation is equivalent to its input by construction, and no uniqueness theorem or prior self-citation is invoked to forbid alternatives. Accordingly, the circularity score is low (1), reflecting the heavy self-compilation but no load-bearing circular step.

Assumptions & free parameters 5 free parameters · 4 assumptions · 1 invented entities

The central claim depends on the choice of several hyperparameters (mask resolution, sample counts, concept counts, Lipschitz constant) and on domain assumptions about the validity of human explanations and the meaningfulness of matrix-factorized concepts. These are not fitted to a single dataset, but they are user-chosen and the conclusions are sensitive to them.

free parameters (5)

Sobol mask resolution = 11x11
Set in Section 2.3.3; determines the granularity of attributions and the computational cost.
Sobol sample size N = 32
Set in Section 2.3.3; yields 3,936 forward passes and is claimed to be sufficient for convergence.
CRAFT concept count (number of factors) = not stated in provided text
CRAFT requires selecting the number of concepts per layer; this is a user-chosen hyperparameter that affects all downstream concept importance results.
Lipschitz constant = 1
The thesis focuses on 1-Lipschitz networks as the explainability-friendly constraint; this is a modeling choice.
Perturbation baseline = 0 or gray
Used throughout for deletion metrics and Sobol inpainting; different baselines change attribution scores.

assumptions (4)

domain assumption The Sobol-Hoeffding decomposition requires the model function to be in L2 and input variables to be independent.
Section 2.3.2 relies on this to define sensitivity indices for attributions.
domain assumption Human explanations (e.g., ClickMe data) provide a valid ground truth for what a model should attend to.
The thesis's claim that attribution methods fail is based on human experiments using such ground truth (Section 2.6).
ad hoc to paper Non-negative matrix factorization of activation vectors yields meaningful, human-interpretable concepts.
CRAFT (Section 4.2) assumes that NMF components correspond to causal features used by the model.
ad hoc to paper 1-Lipschitz networks are representative of robust models and their explanation properties transfer to other architectures.
Section 2.2 and Chapter 3 use 1-Lipschitzness as a proxy for explainability, a choice grounded in prior work but not universally established.

invented entities (1)

Concept as a vector in activation space (CRAFT components) independent evidence
purpose: To provide human-interpretable, model-internal units of explanation that go beyond pixel-level attributions.
The concept is visualized and evaluated with human studies, but the claim that it represents the model's causal features is not independently established.

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Cite this review

Pith. "Pith review of Sparks of Explainability: Recent Advancements in Explaining Large Vision Models." pith.science (2026). https://pith.science/paper/62X2WCFG

@misc{pith2026250201048,
  author       = {Pith},
  title        = {Pith review of: Sparks of Explainability: Recent Advancements in Explaining Large Vision Models},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/62X2WCFG}},
  note         = {Machine review of arXiv:2502.01048}
}

read the original abstract

This thesis explores advanced approaches to improve explainability in computer vision by analyzing and modeling the features exploited by deep neural networks. Initially, it evaluates attribution methods, notably saliency maps, by introducing a metric based on algorithmic stability and an approach utilizing Sobol indices, which, through quasi-Monte Carlo sequences, allows a significant reduction in computation time. In addition, the EVA method offers a first formulation of attribution with formal guarantees via verified perturbation analysis. Experimental results indicate that in complex scenarios these methods do not provide sufficient understanding, particularly because they identify only "where" the model focuses without clarifying "what" it perceives. Two hypotheses are therefore examined: aligning models with human reasoning -- through the introduction of a training routine that integrates the imitation of human explanations and optimization within the space of 1-Lipschitz functions -- and adopting a conceptual explainability approach. The CRAFT method is proposed to automate the extraction of the concepts used by the model and to assess their importance, complemented by MACO, which enables their visualization. These works converge towards a unified framework, illustrated by an interactive demonstration applied to the 1000 ImageNet classes in a ResNet model.

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