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arxiv: 2606.10877 · v1 · pith:7XA4ZKMVnew · submitted 2026-06-09 · 💻 cs.LG · cs.CV

XtrAIn: Training-Guided Occlusion for Feature Attribution

Thodoris Lymperopoulos , Ioannis Kakogeorgiou , Denia Kanellopoulou This is my paper

Pith reviewed 2026-06-27 13:36 UTC · model grok-4.3

classification 💻 cs.LG cs.CV
keywords feature attributionocclusion methodstraining trajectoryparameter updatesmodel interpretabilityattribution shiftXtrAInXstep
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The pith

XtrAIn attributes features by measuring how their linked parameter updates change model outputs along the training trajectory rather than by occluding input values.

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

Occlusion methods estimate feature importance by removing inputs and watching output changes, yet they suffer from biased baselines, out-of-distribution artifacts, and attribution shift in which removing some features alters the apparent role of the rest. XtrAIn moves the occlusion step into parameter space by following the model's actual training path and quantifying the output effect of updates tied to each feature. This produces attribution maps that avoid hand-crafted baselines and remain stable under nonlinear interactions. Experiments on image datasets and PAM50 breast-cancer classification show the resulting patterns are cleaner and easier to interpret than those from standard input-space methods. The approach also supplies a diagnostic view of how feature evidence accumulates as training proceeds.

Core claim

By transferring the occlusion operation from the input space to the parameter space and measuring the effect of feature-associated parameter updates on output logits along the training trajectory, XtrAIn generates attribution scores that sidestep baseline bias and attribution shift, yielding more stable and interpretable explanations than conventional occlusion techniques.

What carries the argument

Training-guided occlusion that follows the model's training trajectory to measure the output impact of updates to parameters associated with individual input features.

If this is right

  • Attribution patterns are cleaner and more interpretable than those produced by standard input-space baselines.
  • Xstep supplies a lightweight approximation that reduces computation while preserving the core training-guided mechanism.
  • The XtrAIn+ variant focuses on target-class-aligned updates to produce more directed explanations.
  • The method functions as a diagnostic for examining how feature-level evidence forms across training epochs.

Where Pith is reading between the lines

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

  • The same training-trajectory lens could be applied to detect when a model begins to rely on spurious correlations by watching which parameter groups update earliest.
  • Comparing XtrAIn attributions across different optimizers or learning-rate schedules might reveal how training choices shape which features the model ultimately treats as important.
  • Extending the approach beyond image and tabular data to sequence models could test whether parameter-update attributions remain stable when features interact over long ranges.

Load-bearing premise

That tracking feature-linked parameter updates during training will reflect true feature importance without introducing fresh biases or shifts from the training dynamics themselves.

What would settle it

Construct a synthetic dataset with known ground-truth feature importances, compute attributions with XtrAIn and with standard occlusion baselines, and check which set of scores recovers the known importances more accurately.

Figures

Figures reproduced from arXiv: 2606.10877 by Denia Kanellopoulou, Ioannis Kakogeorgiou, Thodoris Lymperopoulos.

Figure 1
Figure 1. Figure 1: Illustration of attribution shift in input-space occlu￾sion. In the simple FCNN on the left, all weights are set to one and all biases are zero. The two hidden ReLU neurons there￾fore receive the same input, 𝑥1 + 𝑥2. For 𝑥1 = 𝑥2 = 0.5, both hidden neurons output 1, so the input to the final threshold neuron is 1 + 1 = 2 and the output is activated. However, a small decrease 𝜖 > 0 in 𝑥2 changes both hidden … view at source ↗
Figure 2
Figure 2. Figure 2: Overview of XtrAIn. Given an input sample 𝑥, the method follows the training trajectory of the model and tracks the weights 𝑊 𝑡 𝑖 associated with each input feature 𝑖. For two consecutive model states, 𝑡 and 𝑡 + 1, XtrAIn performs parameter-space occlusion by replacing only the feature-associated weights while keeping all other parameters fixed. The resulting forward and reverse logit changes are combined … view at source ↗
Figure 3
Figure 3. Figure 3: Intermediate update-level attribution patterns for two samples across training epochs. The resulting scores of the update rule are displayed at different steps of a given training epoch (as indicated by their titles), uncovering a diverse set of attribution patterns within the FCNN’s training process. In the early training state of the model, heatmaps indicate higher stability in pattern formation, while i… view at source ↗
Figure 4
Figure 4. Figure 4: Baseline-frontier Pareto analysis of SRG and [PITH_FULL_IMAGE:figures/full_fig_p006_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Baseline-frontier Pareto analysis of SRG and [PITH_FULL_IMAGE:figures/full_fig_p006_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: Bar plots for the top 10 normalized features of the aggregated attribution scores for each class, for the methods SHAP, [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Attribution maps produced by different methods on TMNIST, TMNIST_L, and AMNIST. 𝑋 𝑡𝑟𝐴𝐼𝑛 (XT), 𝑋𝑠𝑡𝑒𝑝 (X_ST) and 𝑋 𝑡𝑟𝐴𝐼𝑛+ (XT+) produce visually cleaner explanations in these examples, featuring a suppressed background and smooth foreground patterns. 9 [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
read the original abstract

Occlusion-based attribution methods provide an intuitive way to estimate feature importance by perturbing input features and measuring the resulting change in model output. However, their reliability is strongly affected by how feature removal is implemented: externally selected baselines can introduce bias, out-of-distribution samples, and unstable explanations, while in nonlinear models the occlusion of a set of features can also alter the contribution of non-occluded features. We refer to this effect as attribution shift, as the attribution scores of the non-occluded features drift from their initial values. To challenge these major issues that render explanations unstable, we introduce XtrAIn, a training-guided attribution method that transfers the occlusion operation from the input space to the parameter space. Instead of replacing input values with hand-crafted baselines, XtrAIn follows the model's training trajectory and measures how feature-associated parameter updates affect the output logits. We further introduce Xstep, a lightweight approximation for reducing computational cost, and XtrAIn+, a target-focused variant that emphasizes updates aligned with the target class. Experiments on controlled image datasets and PAM50 breast-cancer subtype classification show that the proposed methods produce cleaner and more interpretable attribution patterns than standard attribution baselines. Overall, XtrAIn provides a training-aware perspective on feature attribution and offers a useful diagnostic tool for studying how feature-level evidence is formed during training.

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

3 major / 2 minor

Summary. The paper introduces XtrAIn, a training-guided attribution method that transfers the occlusion operation from input space to parameter space by following the model's training trajectory and measuring how feature-associated parameter updates affect output logits. It further proposes Xstep as a lightweight approximation and XtrAIn+ as a target-focused variant. Experiments on controlled image datasets and PAM50 breast-cancer subtype classification are reported to produce cleaner and more interpretable attribution patterns than standard baselines.

Significance. If the central claim holds under quantitative validation, the approach could provide a useful training-aware perspective on feature attribution that mitigates baseline bias and attribution shift, serving as a diagnostic tool for studying feature evidence formation during training in domains such as medical imaging.

major comments (3)
  1. [Abstract] Abstract: the assertion of superior performance and 'cleaner and more interpretable attribution patterns' supplies no quantitative metrics, controls, error analysis, or statistical comparisons, leaving the empirical claims unsupported by the reported evidence.
  2. [Method] Method description: no explicit equation or construction is given for linking a feature to a subset of weights or for isolating feature-associated parameter deltas without introducing new mapping artifacts, rendering the isolation mechanism under-specified.
  3. [Experiments] Experiments section: the description of results on controlled image data and PAM50 classification provides no details on ground-truth attribution comparisons, ablation studies for architecture-specific parameter sharing, or invariance tests, so the claim of freedom from new biases cannot be evaluated.
minor comments (2)
  1. Clarify the computational complexity of Xstep relative to full XtrAIn and provide pseudocode for the approximation.
  2. Ensure all method variants are accompanied by explicit algorithmic steps to support reproducibility.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive feedback. We address each major comment below, indicating planned revisions where appropriate.

read point-by-point responses

  1. Referee: [Abstract] Abstract: the assertion of superior performance and 'cleaner and more interpretable attribution patterns' supplies no quantitative metrics, controls, error analysis, or statistical comparisons, leaving the empirical claims unsupported by the reported evidence.

    Authors: The abstract provides a high-level summary of findings detailed in the Experiments section. We agree that referencing quantitative metrics would better support the claims. We will revise the abstract to include key metrics and statistical comparisons from the results. revision: yes

  2. Referee: [Method] Method description: no explicit equation or construction is given for linking a feature to a subset of weights or for isolating feature-associated parameter deltas without introducing new mapping artifacts, rendering the isolation mechanism under-specified.

    Authors: The Method section describes the conceptual approach of transferring occlusion to parameter space via the training trajectory. We will add explicit equations defining the feature-to-weight association and delta isolation to fully specify the mechanism. revision: yes

  3. Referee: [Experiments] Experiments section: the description of results on controlled image data and PAM50 classification provides no details on ground-truth attribution comparisons, ablation studies for architecture-specific parameter sharing, or invariance tests, so the claim of freedom from new biases cannot be evaluated.

    Authors: Experiments rely on qualitative visual comparisons since ground-truth attributions are unavailable for these tasks. We will add ablation studies on parameter sharing and invariance tests to the revised Experiments section. revision: partial

Circularity Check

0 steps flagged

No significant circularity detected

full rationale

The abstract and method description introduce XtrAIn by transferring occlusion to parameter space and measuring training-trajectory updates, without any equations, derivations, or self-citations shown. No self-definitional reductions, fitted inputs renamed as predictions, or load-bearing self-citations are present. The central claim rests on an independent empirical comparison to baselines rather than reducing to its own inputs by construction. This matches the expectation of a self-contained method description.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review yields no explicit free parameters, axioms, or invented entities; the method implicitly assumes training trajectories encode feature importance without additional postulates stated.

pith-pipeline@v0.9.1-grok · 5782 in / 1080 out tokens · 18915 ms · 2026-06-27T13:36:05.192599+00:00 · methodology

discussion (0)

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