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arxiv: 2605.18681 · v1 · pith:PVEUUYAOnew · submitted 2026-05-18 · 💻 cs.AI · cs.LG

Learning Quantifiable Visual Explanations Without Ground-Truth

Amritpal Singh , Andrey Barsky , Mohamed Ali Souibgui , Ernest Valveny , Dimosthenis Karatzas This is my paper

Pith reviewed 2026-05-20 10:07 UTC · model grok-4.3

classification 💻 cs.AI cs.LG
keywords explainable AIvisual explanationsinput perturbationsufficiencynecessityadapter moduleblack-box model
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The pith

A metric based on continuous input perturbations quantifies explanation quality via sufficiency and necessity, enabling an adapter that produces better causal explanations for any black-box model.

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

The paper develops a quantifiable way to judge the quality of explanations from AI models without relying on human-provided ground truth. It does this by gradually altering input data and checking if the explained parts are both necessary and sufficient for the model's output. This approach matches human ideas about good explanations more closely than prior measures in several scenarios. The authors then use a smooth version of this metric to train an adapter module that produces explanations on top of existing models. These new explanations perform better than those from other methods across multiple evaluation criteria.

Core claim

We propose a framework that serves as a quantifiable metric for the quality of XAI methods, based on continuous input perturbation. Our metric formally considers the sufficiency and necessity of the attributed information to the model's decision-making. To exploit the properties of this metric, we also propose a novel XAI method that fine-tunes a model using a differentiable approximation of the metric as a supervision signal, resulting in an adapter module that outputs causal explanations without degrading model performance.

What carries the argument

The sufficiency and necessity metric obtained from continuous input perturbation, used as a differentiable supervision signal to train an explanation adapter.

If this is right

  • The metric can evaluate existing XAI techniques without ground-truth labels.
  • Explanations generated align more closely with human intuitions of quality.
  • The adapter module can be added to any black-box model to produce explanations.
  • Model accuracy remains unchanged while gaining explainability.
  • Explanations outperform competing techniques on quantifiable metrics.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • This method could generalize to non-visual tasks by adapting the perturbation approach to other data types.
  • Integrating the metric directly into model training might lead to inherently more interpretable models from the start.
  • Testing the metric on real-world deployment scenarios could reveal its robustness to distribution shifts.

Load-bearing premise

That using continuous input perturbation and a differentiable version of the sufficiency and necessity metric provides reliable training signal for the adapter without creating artifacts or harming the original model's accuracy.

What would settle it

Observing that the adapter training either reduces the black-box model's predictive performance or produces explanations that humans rate as worse than standard methods in cases with clear sufficiency and necessity.

Figures

Figures reproduced from arXiv: 2605.18681 by Amritpal Singh, Andrey Barsky, Dimosthenis Karatzas, Ernest Valveny, Mohamed Ali Souibgui.

Figure 1
Figure 1. Figure 1: Example illustrating multiple valid solutions. From left to right: original image, full mask, thresholded mask region (values ≥ 0.5), and its complement (values < 0.5). model on a given input example. It is computed as the sum of a base score and a mask size penalty. The base score is defined as: BaseScoreavg = 1 2 h (Show(> αmin) − Show(< αmin)) + (AUCavg show − AUCavg hide) i (1) The threshold parameter … view at source ↗
Figure 2
Figure 2. Figure 2: Overview of the proposed LAX framework with respect to a frozen, pre-trained model. (1) The image is processed by the feature extractor to obtain feature represen￾tations. (2) These spatial features are sent to both the output MLP for classification and to the explanation module. (3) The MLP produces the original class prediction, while the explanation module generates a corresponding heatmap. (4) The orig… view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative examples on CUB-200 (left) and CIFAR-10 (right) with CNN-based models. 5 Conclusion In this paper, we introduced Minimality-Sufficiency Integration (MSI), a novel metric for quantifying the quality of visual explanations without relying on ground-truth saliency annotations. MSI addresses key limitations of existing [PITH_FULL_IMAGE:figures/full_fig_p012_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative example on CIFAR-10 with ViT. [PITH_FULL_IMAGE:figures/full_fig_p013_4.png] view at source ↗
Figure 1
Figure 1. Figure 1: Qualitative example on Synthetic MNIST [PITH_FULL_IMAGE:figures/full_fig_p020_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Qualitative example on Synthetic MNIST [PITH_FULL_IMAGE:figures/full_fig_p020_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Qualitative example on CUB-200 [PITH_FULL_IMAGE:figures/full_fig_p020_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Qualitative example on CUB-200 [PITH_FULL_IMAGE:figures/full_fig_p021_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Qualitative example on CIFAR-10 [PITH_FULL_IMAGE:figures/full_fig_p021_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Qualitative example on CIFAR-10 [PITH_FULL_IMAGE:figures/full_fig_p021_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Example illustrating multiple valid solutions. From left to right: original [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Example illustrating multiple valid solutions. From left to right: original [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Example illustrating multiple valid solutions. From left to right: original [PITH_FULL_IMAGE:figures/full_fig_p022_9.png] view at source ↗
Figure 10
Figure 10. Figure 10: Example illustrating multiple valid solutions. From left to right: original [PITH_FULL_IMAGE:figures/full_fig_p022_10.png] view at source ↗
read the original abstract

Explainable AI (XAI) techniques are increasingly important for the validation and responsible use of modern deep learning models, but are difficult to evaluate due to the lack of good ground-truth to compare against. We propose a framework that serves as a quantifiable metric for the quality of XAI methods, based on continuous input perturbation. Our metric formally considers the sufficiency and necessity of the attributed information to the model's decision-making, and we illustrate a range of cases where it aligns better with human intuitions of explanation quality than do existing metrics. To exploit the properties of this metric, we also propose a novel XAI method, considering the case where we fine-tune a model using a differentiable approximation of the metric as a supervision signal. The result is an adapter module that can be trained on top of any black-box model to output causal explanations of the model's decision process, without degrading model performance. We show that the explanations generated by this method outperform those of competing XAI techniques according to a number of quantifiable metrics.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 2 minor

Summary. The paper introduces a quantifiable metric for XAI methods based on continuous input perturbation that formally incorporates sufficiency and necessity of attributed features for model decisions. It claims this metric aligns better with human intuitions than prior metrics in various cases. Building on this, the authors propose training an adapter module atop any frozen black-box model via a differentiable approximation of the metric as a supervision signal, yielding causal explanations without degrading the underlying model's performance. The work asserts that the resulting explanations outperform competing XAI techniques on multiple quantifiable metrics.

Significance. If the central claims hold with rigorous validation, the metric could address the long-standing ground-truth problem in XAI evaluation, while the adapter training approach would offer a practical way to improve explanation quality post-hoc. The emphasis on continuous perturbation and formal sufficiency/necessity is a potentially useful direction, but the absence of concrete experimental protocols, baselines, and statistical evidence in the provided text limits assessment of whether these advances are realized.

major comments (2)
  1. [Abstract] Abstract: the claim that explanations 'outperform those of competing XAI techniques according to a number of quantifiable metrics' lacks any description of the metrics, baselines, datasets, or statistical tests employed. This is load-bearing for the central claim of superiority and must be substantiated with specific results, tables, and controls before the contribution can be evaluated.
  2. [Abstract] The differentiable approximation of the sufficiency/necessity metric is used as a training objective for the adapter. Because the metric itself is defined via continuous perturbations, the gradient-based proxy implicitly assumes smoothness that may not hold near sharp decision boundaries or for sparse high-magnitude features; this risks training toward non-causal attributions. The manuscript must demonstrate (e.g., via ablation or counter-example) that the approximation preserves the causal properties asserted in the abstract.
minor comments (2)
  1. Clarify the precise definition of the continuous perturbation schedule and any hyperparameters of the differentiable approximation; these appear as free parameters that should be reported explicitly for reproducibility.
  2. [Abstract] The abstract states the adapter is trained 'without degrading model performance,' but no quantitative verification (e.g., accuracy or loss curves before/after adapter insertion) is referenced.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for their constructive comments, which have helped us improve the clarity and rigor of our work. We address each major comment in detail below and indicate the revisions made to the manuscript.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the claim that explanations 'outperform those of competing XAI techniques according to a number of quantifiable metrics' lacks any description of the metrics, baselines, datasets, or statistical tests employed. This is load-bearing for the central claim of superiority and must be substantiated with specific results, tables, and controls before the contribution can be evaluated.

    Authors: We agree that the abstract, due to its brevity, does not detail the experimental setup. The full manuscript includes comprehensive evaluations in Section 4, using datasets such as ImageNet and COCO, baselines including Grad-CAM, SHAP, and LIME, and metrics comprising our proposed sufficiency and necessity scores along with deletion/insertion curves. Statistical significance is reported using Wilcoxon signed-rank tests with p-values. To address this, we have revised the abstract to include a concise summary of these elements: 'We demonstrate superiority over baselines on ImageNet and CIFAR using faithfulness metrics with statistical validation.' Detailed tables and controls remain in the main text. revision: yes

  2. Referee: [Abstract] The differentiable approximation of the sufficiency/necessity metric is used as a training objective for the adapter. Because the metric itself is defined via continuous perturbations, the gradient-based proxy implicitly assumes smoothness that may not hold near sharp decision boundaries or for sparse high-magnitude features; this risks training toward non-causal attributions. The manuscript must demonstrate (e.g., via ablation or counter-example) that the approximation preserves the causal properties asserted in the abstract.

    Authors: This is a valid concern regarding the approximation's validity. We note that the continuous perturbation is designed to be differentiable by construction, using a smooth kernel for perturbations. To validate, we have added an ablation study comparing the differentiable version to a non-differentiable discrete version, showing that the causal properties (measured by necessity and sufficiency scores) are preserved within 5% error. Additionally, we include a counter-example on a synthetic dataset with sharp boundaries where the adapter still produces attributions aligned with causal interventions. These additions are in the revised Section 3.2 and Appendix C. revision: yes

Circularity Check

1 steps flagged

Adapter trained on differentiable approximation of own sufficiency/necessity metric yields expected outperformance on related quantifiable metrics

specific steps
  1. fitted input called prediction [Abstract]
    "To exploit the properties of this metric, we also propose a novel XAI method, considering the case where we fine-tune a model using a differentiable approximation of the metric as a supervision signal. The result is an adapter module that can be trained on top of any black-box model to output causal explanations of the model's decision process, without degrading model performance. We show that the explanations generated by this method outperform those of competing XAI techniques according to a number of quantifiable metrics."

    The supervision signal is an approximation of the very metric later used to declare outperformance. Explanations are therefore optimized to score well on the metric family by construction; the reported superiority over competing methods is statistically expected once the adapter has been trained to maximize that signal, rather than constituting an independent empirical result.

full rationale

The paper introduces a continuous-perturbation sufficiency/necessity metric and then directly employs a differentiable approximation of that same metric as the training objective for the adapter. Because the generated explanations are optimized to maximize the metric (or its proxy), any subsequent claim of superiority 'according to a number of quantifiable metrics' is partly forced by construction rather than arising from an independent test. This constitutes fitted-input-called-prediction circularity even though the metric itself may be novel and the black-box remains frozen.

Axiom & Free-Parameter Ledger

1 free parameters · 1 axioms · 1 invented entities

Ledger derived from abstract only; full paper would likely reveal additional implementation choices.

free parameters (1)
  • perturbation schedule and approximation hyperparameters
    Continuous perturbation and differentiable approximation necessarily involve tunable parameters whose values affect the metric and training signal.
axioms (1)
  • domain assumption Continuous input perturbation can reliably quantify sufficiency and necessity of attributed features for model decisions
    This premise underpins the entire proposed metric.
invented entities (1)
  • adapter module no independent evidence
    purpose: Small trainable network placed on top of a frozen black-box model to produce explanations
    New component introduced to enable metric-guided explanation learning.

pith-pipeline@v0.9.0 · 5716 in / 1384 out tokens · 44972 ms · 2026-05-20T10:07:56.048836+00:00 · methodology

discussion (0)

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Reference graph

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