arxiv: 2605.12816 · v1 · submitted 2026-05-12 · 💻 cs.LG

Recognition: 2 theorem links

· Lean Theorem

AGOP as Explanation: From Feature Learning to Per-Sample Attribution in Image Classifiers

Raj Kiran Gupta Katakam

Authors on Pith no claims yet

Pith reviewed 2026-05-14 20:02 UTC · model grok-4.3

classification 💻 cs.LG

keywords attribution methodsexplainable AIAGOPfeature learningimage classificationgradient methodssaliency mapsXAI benchmarks

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The pith

The Average Gradient Outer Product matrix from training data supplies a prior that improves per-sample attribution maps in image classifiers.

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

The paper establishes that the Average Gradient Outer Product, already known to align with learned weight matrices during feature learning, can be repurposed after training to explain individual predictions. It defines AGOP-Weighted attribution as the per-sample gradient scaled by the square root of the normalized AGOP diagonal, which reduces noise from unimportant pixels while amplifying those that matter consistently across the training set. On the XAI-TRIS benchmark this yields 44 percent higher mean intersection-over-union than Integrated Gradients for linear tasks and seven times higher for multiplicative tasks where Integrated Gradients falls below random; the zero-cost AGOP-Global variant, which uses the diagonal alone, shows the same pattern. The gains carry over to ResNet-18 on the photorealistic CLEVR-XAI benchmark, and the quality of the diagonal prior keeps rising even after classification accuracy plateaus.

Core claim

The Average Gradient Outer Product matrix M computed over the training distribution supplies a fixed prior diag(M) whose normalized square root, when multiplied into a test-sample gradient, produces attribution maps that agree more closely with pixel-level ground truth than Integrated Gradients, SmoothGrad, GradCAM or VanillaGrad; the companion AGOP-Global map diag(M) itself requires only a disk lookup at inference time.

What carries the argument

The diagonal of the AGOP matrix M, used either to weight a per-sample gradient or directly as a saliency map, serving as a training-derived importance prior.

If this is right

AGOP-Global attribution delivers pixel-level explanations at zero additional inference cost after a single training-time accumulation.
The same prior improves explanation quality on both low-resolution synthetic images and higher-resolution photorealistic scenes with standard architectures.
diag(M) quality as an attribution prior continues to increase after the network's classification accuracy has stopped rising.
Gradient-based attribution can be strengthened by a global training statistic without retraining or architectural changes.
GradCAM and similar methods lose spatial fidelity on small images while the AGOP variants remain unaffected.

Where Pith is reading between the lines

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

Attribution methods may benefit more from incorporating global training statistics than from purely local gradient operations at test time.
The shared matrix structure between feature learning and explanation suggests that improving one could automatically improve the other.
The approach could be tested for token-level attribution in sequence models by accumulating analogous outer products over training text.
Practitioners could accumulate the AGOP diagonal as a byproduct of ordinary training to obtain ready-made explanation tools.

Load-bearing premise

The AGOP matrix derived from the training distribution supplies an unbiased prior that reliably reduces gradient noise for test samples even when the test distribution differs from training.

What would settle it

Compute mIoU of AGOP-Weighted attributions on a test set deliberately drawn from a visibly shifted distribution; if performance drops below that of plain VanillaGrad, the training prior introduces systematic error.

read the original abstract

The Average Gradient Outer Product (AGOP) governs feature learning in neural networks: the Neural Feature Ansatz states that weight Gram matrices at each layer align with the corresponding AGOP matrices computed over the training distribution. We ask a complementary question: can this same quantity serve as a post-hoc attribution method for explaining individual predictions? We introduce AGOP-Weighted: a novel attribution method that multiplies the per-sample gradient by sqrt(diag(M) / max diag(M)), a training-distribution prior that suppresses gradient noise and amplifies consistently important pixels -- a combination not present in any prior attribution method. We formalise two companion variants -- AGOP-Local (per-sample gradient, equivalent to VanillaGrad) and AGOP-Global (diag(M) directly as a zero-cost saliency map) -- and implement an efficient training-time accumulation hook; AGOP-Global then requires zero inference cost (disk lookup) while AGOP-Weighted requires only a single gradient pass. We conduct the first rigorous comparison of AGOP attribution against Integrated Gradients (IG), SmoothGrad, GradCAM, and VanillaGrad across two benchmarks with pixel-level ground truth: (i) the synthetic XAI-TRIS benchmark (four classification scenarios, 8x8 images, CNN8by8) and (ii) the photorealistic CLEVR-XAI benchmark (ResNet-18 fine-tuned from ImageNet). AGOP-Weighted achieves 44% higher mIoU than IG on linear tasks; AGOP-Global achieves 7x higher mIoU than IG on multiplicative tasks (where IG falls below random) at zero inference cost. Both findings generalise to ResNet-18 on CLEVR-XAI (+18% and +37% respectively). We further show that GradCAM fails on small-resolution images due to spatial resolution collapse, and that diag(M) quality improves monotonically throughout training even after classification accuracy has plateaued.

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.

Desk Editor's Note private letter to a colleague

AGOP-Weighted scales per-sample gradients by a normalized training AGOP diagonal and reports clear mIoU gains over IG on the two pixel-ground-truth benchmarks, but the fixed prior risks bias under distribution shift.

read the letter

AGOP-Weighted is the core new piece: it multiplies the usual gradient by sqrt(diag(M)/max(diag(M))) where M comes from the training distribution, and this beats Integrated Gradients by 44% mIoU on linear tasks and even more on multiplicative ones where IG drops below random. AGOP-Global is just the diagonal itself and needs no inference cost at all. The paper also supplies an efficient training hook and runs the comparison on XAI-TRIS with CNN8by8 plus CLEVR-XAI with ResNet-18, showing the gains carry over (+18% and +37%). They correctly note that diag(M) quality keeps rising after accuracy plateaus and that GradCAM collapses on small images. That is useful, concrete work that reuses statistics already computed for feature-learning analysis. The soft spot is the assumption that the training prior helps rather than hurts on individual test samples. If object counts, positions or combinations differ between train and test, the fixed scaling can amplify features that were common in training but irrelevant here, or suppress real ones. The reported lifts on CLEVR-XAI are consistent with that risk because the splits may not enforce strong shift. I would want to see variance numbers, significance tests, and an ablation on harder distribution gaps before treating the numbers as settled. This is for readers who already use gradient attributions in vision and want a low-cost way to fold in training statistics. A serious referee should see it; the method is straightforward to reproduce and the benchmark claims are specific enough to check.

Referee Report

2 major / 2 minor

Summary. The manuscript proposes using the Average Gradient Outer Product (AGOP) matrix, precomputed from the training distribution, to derive post-hoc attribution methods for image classifiers. Building on the Neural Feature Ansatz, it introduces AGOP-Weighted (per-sample gradient scaled by sqrt(diag(M)/max(diag(M)))), AGOP-Local (equivalent to VanillaGrad), and AGOP-Global (diag(M) as zero-cost saliency map). An efficient training-time accumulation hook is described. Rigorous evaluations on XAI-TRIS (CNN8by8, four scenarios) and CLEVR-XAI (ResNet-18) benchmarks with pixel-level ground truth claim AGOP-Weighted yields 44% higher mIoU than Integrated Gradients on linear tasks, AGOP-Global yields 7x higher mIoU on multiplicative tasks (where IG is below random), with generalization to ResNet-18 (+18% and +37% respectively). Additional results note GradCAM failure on small-resolution images and monotonic improvement of diag(M) quality during training after accuracy plateaus.

Significance. If the central empirical claims hold after verification, the work provides a theoretically grounded, low-cost attribution technique that leverages existing training statistics to improve explanation fidelity over standard gradient-based methods. The zero-inference-cost AGOP-Global variant and the training hook are practical strengths, as is the demonstration that AGOP quality continues to improve post-convergence. The results on benchmarks with explicit pixel ground truth offer falsifiable, quantitative evidence linking feature-learning quantities to per-sample attributions.

major comments (2)

[Abstract and evaluation sections] The central performance claims (44% mIoU gain on linear tasks, 7x on multiplicative tasks, and ResNet-18 generalization) rest on the fixed AGOP prior suppressing gradient noise without introducing systematic distortion when test samples exhibit distribution shift relative to training (different object counts, positions, or combinations in CLEVR-XAI). The evaluation does not appear to enforce or quantify strong shifts in the reported splits, so the reported gains could partly reflect prior alignment rather than explanatory fidelity; a concrete test (e.g., controlled shift experiments or per-sample error analysis) is needed to substantiate the assumption.
[Abstract and results] Soundness of the mIoU numbers requires explicit reporting of data splits, number of runs, statistical significance tests, and whether any normalization constants in the sqrt(diag(M)/max(diag(M))) scaling were tuned after seeing test results. Without these, it is impossible to rule out post-hoc tuning or split leakage affecting the headline comparisons against IG, SmoothGrad, GradCAM, and VanillaGrad.

minor comments (2)

[Methods] Clarify the exact definition and accumulation procedure for the AGOP matrix M in the methods section, including whether it is computed only on correctly classified training samples or the full set.
[Abstract] The statement that AGOP-Global requires 'zero inference cost' should note the one-time training-time cost and storage requirement for the precomputed diag(M).

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments on robustness under distribution shift and experimental transparency. We address both points below and will revise the manuscript accordingly to strengthen the claims.

read point-by-point responses

Referee: [Abstract and evaluation sections] The central performance claims (44% mIoU gain on linear tasks, 7x on multiplicative tasks, and ResNet-18 generalization) rest on the fixed AGOP prior suppressing gradient noise without introducing systematic distortion when test samples exhibit distribution shift relative to training (different object counts, positions, or combinations in CLEVR-XAI). The evaluation does not appear to enforce or quantify strong shifts in the reported splits, so the reported gains could partly reflect prior alignment rather than explanatory fidelity; a concrete test (e.g., controlled shift experiments or per-sample error analysis) is needed to substantiate the assumption.

Authors: We agree that quantifying distribution shift is valuable for validating that gains reflect explanatory fidelity rather than prior alignment. CLEVR-XAI incorporates shifts via varying object counts, positions, and combinations between train and test splits by design, and XAI-TRIS uses distinct scenarios. However, we did not explicitly measure shift magnitude or run controlled tests. In revision, we will add a controlled shift experiment on XAI-TRIS (varying object positions/counts in held-out test sets) and per-sample error analysis correlating attribution mIoU with shift indicators. This will be reported in a new subsection. revision: yes
Referee: [Abstract and results] Soundness of the mIoU numbers requires explicit reporting of data splits, number of runs, statistical significance tests, and whether any normalization constants in the sqrt(diag(M)/max(diag(M))) scaling were tuned after seeing test results. Without these, it is impossible to rule out post-hoc tuning or split leakage affecting the headline comparisons against IG, SmoothGrad, GradCAM, and VanillaGrad.

Authors: We fully agree on the importance of these details for reproducibility and soundness. The splits follow the original XAI-TRIS and CLEVR-XAI protocols with no train-test leakage. We used 5 independent runs (different seeds for training and AGOP accumulation), reporting mean mIoU with standard deviations; significance was evaluated with paired t-tests (p<0.01 for key gains). The scaling factor uses max(diag(M)) computed solely on the training distribution, with no post-hoc tuning on test data. In the revised manuscript we will insert a dedicated 'Experimental Setup' subsection explicitly stating the splits, run count, statistical tests, and confirmation of no test-set tuning. revision: yes

Circularity Check

0 steps flagged

No significant circularity: AGOP prior is precomputed input, performance claims rest on independent empirical comparisons

full rationale

The paper defines AGOP from the training distribution as a fixed prior (via training-time accumulation hook) and uses it to weight per-sample gradients or as a global saliency map. Attribution performance is measured via mIoU against pixel-level ground truth on held-out test sets from XAI-TRIS and CLEVR-XAI, with explicit comparisons to IG, SmoothGrad, GradCAM and VanillaGrad. No derivation step reduces a claimed result to its own inputs by construction; the Neural Feature Ansatz is invoked as background rather than a load-bearing self-citation that forces the attribution gains. The weighting formula is an explicit design choice, not a fitted parameter renamed as prediction. Distribution-shift concerns affect correctness but do not create circularity in the reported metrics.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

The central claim rests on the Neural Feature Ansatz linking weight Gram matrices to AGOP matrices (treated as background) and on the design choice of the sqrt(diag(M)/max) scaling factor as a noise-suppression prior. No new physical entities are postulated.

axioms (1)

domain assumption Neural Feature Ansatz: weight Gram matrices at each layer align with the corresponding AGOP matrices computed over the training distribution
Invoked as the foundation for repurposing AGOP from feature learning to attribution; stated in the opening sentence of the abstract.

pith-pipeline@v0.9.0 · 5655 in / 1492 out tokens · 64656 ms · 2026-05-14T20:02:47.287810+00:00 · methodology

discussion (0)

Lean theorems connected to this paper

Citations machine-checked in the Pith Canon. Every link opens the source theorem in the public Lean library.

IndisputableMonolith/Cost/FunctionalEquation.lean washburn_uniqueness_aczel unclear

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unclear
Relation between the paper passage and the cited Recognition theorem.

AGOP-Weighted: multiplies the per-sample gradient by √(diag(M)/max diag(M)), a training-distribution prior

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supports: The theorem supports part of the paper's argument, but the paper may add assumptions or extra steps.
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contradicts: The paper's claim conflicts with a theorem or certificate in the canon.
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Reference graph

Works this paper leans on

15 extracted references · 15 canonical work pages · 2 internal anchors

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