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arxiv: 2604.05819 · v1 · submitted 2026-04-07 · 💻 cs.CV · cs.LG

Recognition: no theorem link

Learn to Rank: Visual Attribution by Learning Importance Ranking

David Schinagl , Christian Fruhwirth-Reisinger , Alexander Prutsch , Samuel Schulter , Horst Possegger
Authors on Pith no claims yet

Pith reviewed 2026-05-10 19:48 UTC · model grok-4.3

classification 💻 cs.CV cs.LG
keywords visual attributionexplainable AIlearning to rankGumbel-Sinkhornvision transformersdeletion insertion metricsperturbation explanations
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The pith

A method learns to rank pixel importance by directly optimizing deletion and insertion scores using a differentiable relaxation of sorting.

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

The paper establishes a learning scheme for visual attribution that optimizes deletion and insertion metrics directly instead of surrogate objectives or heuristic teachers. A sympathetic reader would care because this could resolve the efficiency, bias, and coarseness issues in current explanation methods for complex vision models. The approach frames attribution as a permutation learning task and applies a Gumbel-Sinkhorn relaxation to make the non-differentiable ranking operations differentiable, enabling gradient-based end-to-end training via attribution-guided perturbations. During inference the trained model generates dense pixel-level maps in one forward pass, with optional gradient refinement.

Core claim

The authors claim that framing visual attribution as importance ranking and replacing hard sorting with the Gumbel-Sinkhorn differentiable relaxation allows direct optimization of deletion and insertion metrics. This produces an end-to-end trainable explainer that perturbs the target model during training and yields sharper, boundary-aligned, pixel-level attributions at inference time, with measured quantitative gains especially on transformer-based vision models.

What carries the argument

The Gumbel-Sinkhorn relaxation of sorting, which converts the discrete ranking operation into a differentiable soft permutation matrix that supports gradient flow when computing deletion and insertion metrics.

If this is right

  • Attribution maps can be produced in a single forward pass after training instead of repeated expensive perturbations.
  • Explanations become denser and more pixel-precise rather than limited to patch-level outputs on transformers.
  • The same trained explainer works across different target models once the ranking-based training is complete.
  • Optional few-step gradient refinement can be applied post hoc to further sharpen the maps without retraining.

Where Pith is reading between the lines

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

  • The ranking formulation might transfer to explanation tasks in other domains where ordered feature importance is meaningful.
  • If the relaxation remains stable, the approach could reduce the cost barrier that currently separates perturbation-based causal methods from fast learning-based ones.
  • Wider adoption would depend on whether the learned attributions remain reliable when the target model is updated or fine-tuned.

Load-bearing premise

The Gumbel-Sinkhorn relaxation must be accurate enough that gradients through the relaxed ranking can effectively drive optimization of the original non-differentiable deletion and insertion metrics.

What would settle it

Measure deletion and insertion scores of the learned attributions against existing baselines on a held-out ImageNet validation set using vision transformer backbones; the claim is falsified if no consistent improvement in those scores or in boundary alignment appears.

Figures

Figures reproduced from arXiv: 2604.05819 by Alexander Prutsch, Christian Fruhwirth-Reisinger, David Schinagl, Horst Possegger, Samuel Schulter.

Figure 1
Figure 1. Figure 1: Qualitative comparison of attribution maps [PITH_FULL_IMAGE:figures/full_fig_p003_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Soft deletion masks. Given an input image I and its attribution map A, the soft top-k masks m soft,(k) A progressively replace the most important regions with the reference image. Unlike hard binary masks, the soft relaxation (Sec. 3.1) assigns continuous importance values to each region, enabling gradient-based optimization of the deletion and insertion metrics. Note the varying grid resolution G across e… view at source ↗
Figure 3
Figure 3. Figure 3: Effect of (a) test-time refinement steps T and (b) training perturbation steps S on the Insertion AUC. Results are shown for the predicted class (Pred.) and the ground-truth class (GT) on the ImageNet validation set (ViT-B/16). or LeGrad, while achieving competitive or superior faithfulness compared to methods that are orders of magnitude slower. When refining over T=3 gradient steps, the runtime increases… view at source ↗
Figure 4
Figure 4. Figure 4: Exemplary test-time refinement. Attribution maps produced by AHA without (T=0) and with (T=5) test-time refinement for the predicted class using ViT￾B/16. Refinement progressively sharpens the heatmaps, concentrating attribution on the relevant object parts while suppressing diffuse background activations. from 0.138 to 0.121 for T = 0 → 3. The qualitative examples in [PITH_FULL_IMAGE:figures/full_fig_p01… view at source ↗
Figure 5
Figure 5. Figure 5: Out-of-distribution shortcut detection. A ViT-B/16 is fine-tuned with a white circle injected into every sunglasses training image. Using an AHA explainer trained on the clean model, test-time refinement progressively reveals the learned short￾cut: at T=0 the attribution highlights generic class features, while increasing T reveals the white circle as important feature for the fine-tuned model’s decision. … view at source ↗
Figure 6
Figure 6. Figure 6: Deletion and Insertion AUC curves averaged over all reference images (I0 ∈ black, mean, blur) on ImageNet validation samples. Each row corresponds to a classifier–class combination (ViT-B/16 and ViT-B/32, predicted and ground-truth class). For Deletion (left), a steeper drop indicates better attribution; for Insertion (right), a steeper rise indicates better attribution [PITH_FULL_IMAGE:figures/full_fig_p… view at source ↗
Figure 7
Figure 7. Figure 7: Qualitative comparison of attribution maps [PITH_FULL_IMAGE:figures/full_fig_p027_7.png] view at source ↗
Figure 8
Figure 8. Figure 8: Qualitative comparison of attribution maps [PITH_FULL_IMAGE:figures/full_fig_p028_8.png] view at source ↗
Figure 9
Figure 9. Figure 9: Qualitative comparison of attribution maps [PITH_FULL_IMAGE:figures/full_fig_p029_9.png] view at source ↗
read the original abstract

Interpreting the decisions of complex computer vision models is crucial to establish trust and accountability, especially in safety-critical domains. An established approach to interpretability is generating visual attribution maps that highlight regions of the input most relevant to the model's prediction. However, existing methods face a three-way trade-off. Propagation-based approaches are efficient, but they can be biased and architecture-specific. Meanwhile, perturbation-based methods are causally grounded, yet they are expensive and for vision transformers often yield coarse, patch-level explanations. Learning-based explainers are fast but usually optimize surrogate objectives or distill from heuristic teachers. We propose a learning scheme that instead optimizes deletion and insertion metrics directly. Since these metrics depend on non-differentiable sorting and ranking, we frame them as permutation learning and replace the hard sorting with a differentiable relaxation using Gumbel-Sinkhorn. This enables end-to-end training through attribution-guided perturbations of the target model. During inference, our method produces dense, pixel-level attributions in a single forward pass with optional, few-step gradient refinement. Our experiments demonstrate consistent quantitative improvements and sharper, boundary-aligned explanations, particularly for transformer-based vision models.

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 / 1 minor

Summary. The paper proposes a learning-based visual attribution method that directly optimizes deletion and insertion metrics by framing ranking as a permutation learning problem. It replaces hard sorting with a Gumbel-Sinkhorn differentiable relaxation to enable end-to-end training of an attribution network via attribution-guided perturbations. At inference, it produces dense pixel-level attributions in a single forward pass (with optional few-step gradient refinement) and claims consistent quantitative improvements plus sharper, boundary-aligned explanations, especially for transformer-based vision models.

Significance. If the Gumbel-Sinkhorn relaxation proves accurate at scale, the approach would usefully combine the speed of learned explainers with the causal grounding of perturbation methods, addressing a noted weakness of current ViT attribution techniques. The direct optimization of the target metrics (rather than surrogates) is a conceptually strong contribution, and the focus on dense pixel-level output for transformers fills a practical gap. However, the absence of any quantitative results, baselines, or ablation details in the abstract makes the magnitude of improvement impossible to assess from the provided text.

major comments (2)
  1. [Method (Gumbel-Sinkhorn relaxation and training objective)] The central claim depends on the Gumbel-Sinkhorn relaxation yielding gradients that meaningfully optimize the non-differentiable deletion/insertion scores for dense pixel rankings (~50k elements for 224×224 images). No equation or experiment in the manuscript demonstrates that the soft permutation matrix correlates tightly with the hard metric or that the learned attributions remain stable under temperature annealing; this is load-bearing for the end-to-end training argument.
  2. [Abstract and Experiments section] The abstract asserts 'consistent quantitative improvements' and 'sharper, boundary-aligned explanations' yet supplies no numbers, baseline comparisons, statistical tests, ablation studies, or dataset details. Without these, the support for the central claim cannot be evaluated.
minor comments (1)
  1. [Abstract] The abstract would be strengthened by including at least one key quantitative result (e.g., deletion/insertion AUC deltas versus a standard baseline) to substantiate the improvement claims.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback and for recognizing the potential of our end-to-end optimization approach. We address each major comment below and will incorporate revisions to strengthen the manuscript.

read point-by-point responses

  1. Referee: [Method (Gumbel-Sinkhorn relaxation and training objective)] The central claim depends on the Gumbel-Sinkhorn relaxation yielding gradients that meaningfully optimize the non-differentiable deletion/insertion scores for dense pixel rankings (~50k elements for 224×224 images). No equation or experiment in the manuscript demonstrates that the soft permutation matrix correlates tightly with the hard metric or that the learned attributions remain stable under temperature annealing; this is load-bearing for the end-to-end training argument.

    Authors: We agree that explicit validation of the relaxation's fidelity is important for the end-to-end claim. The manuscript already defines the Gumbel-Sinkhorn operator and its temperature-controlled soft permutation matrix in Section 3.2 (Equation 4), drawing on its established convergence properties to the hard permutation as temperature approaches zero. However, to directly address the referee's concern, we will add a new subsection with an empirical study: we will report Pearson correlation between soft and hard deletion/insertion scores across a range of temperatures on held-out images, plus stability plots under annealing schedules. This addition will be placed in the Experiments section and will not alter the core method. revision: yes

  2. Referee: [Abstract and Experiments section] The abstract asserts 'consistent quantitative improvements' and 'sharper, boundary-aligned explanations' yet supplies no numbers, baseline comparisons, statistical tests, ablation studies, or dataset details. Without these, the support for the central claim cannot be evaluated.

    Authors: We acknowledge that the current abstract is high-level and does not contain numerical results. The full manuscript already reports these details in Section 4, including deletion/insertion AUC improvements over baselines (e.g., Grad-CAM, RISE, and learned explainers) on ImageNet and additional datasets, with statistical significance via paired t-tests, ablations on temperature and refinement steps, and qualitative boundary-alignment metrics. To resolve the referee's valid point, we will revise the abstract to include the key quantitative gains, mention the primary datasets, and briefly note the ablation findings. This change will make the claims evaluable from the abstract while preserving its length constraints. revision: yes

Circularity Check

0 steps flagged

No circularity: derivation introduces independent relaxation and objective

full rationale

The paper's chain frames deletion/insertion metrics as a permutation-learning problem and substitutes hard ranking with the Gumbel-Sinkhorn relaxation to enable end-to-end gradient training. This is presented as a new scheme rather than a re-derivation of prior fitted quantities. No equation reduces the learned attributions or the optimized metrics back to the inputs by construction, and no load-bearing uniqueness theorem or self-citation is invoked to force the result. The central claim therefore remains an independent modeling choice whose validity rests on empirical correlation between the relaxed and hard objectives, not on definitional equivalence.

Axiom & Free-Parameter Ledger

1 free parameters · 2 axioms · 0 invented entities

The central claim rests on the validity of deletion/insertion as faithful proxies and on the Gumbel-Sinkhorn approximation being close enough for gradient descent to succeed.

free parameters (1)
  • Gumbel-Sinkhorn temperature
    Hyperparameter controlling the smoothness of the ranking relaxation; must be chosen or tuned.
axioms (2)
  • domain assumption Deletion and insertion metrics are appropriate causal proxies for attribution quality.
    Invoked when the paper states it optimizes these metrics directly.
  • domain assumption The differentiable relaxation preserves sufficient gradient signal for end-to-end training.
    Required for the claim that end-to-end training through perturbations is feasible.

pith-pipeline@v0.9.0 · 5511 in / 1136 out tokens · 54288 ms · 2026-05-10T19:48:12.630808+00:00 · methodology

discussion (0)

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Reference graph

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