arxiv: 2605.13340 · v1 · submitted 2026-05-13 · 💻 cs.LG

Recognition: unknown

Shortcut Mitigation via Spurious-Positive Samples

Phuong Quynh Le , J\"org Schl\"otterer , Sari Sadiya , Gemma Roig , Christin Seifert

Authors on Pith no claims yet

Pith reviewed 2026-05-14 19:10 UTC · model grok-4.3

classification 💻 cs.LG

keywords shortcut mitigationspurious attributesneuron regularizationmodel robustnessdistributional robustnessbias reductionmachine learning fairness

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The pith

Identifying a small set of instances where models rely on spurious attributes and regularizing the associated neurons improves robustness without needing extra annotations or balanced data.

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

The paper shows how to mitigate shortcut learning in models by locating a small number of training examples that trigger reliance on spurious features. It then identifies the most influential neurons in an intermediate layer for those examples and reduces their contribution through regularization. This targeted intervention encourages the model to use informative features instead. As a result, robustness improves even when training data lacks complete group coverage or additional validation sets. Readers should care because many real-world datasets contain hidden shortcuts that cause models to fail unexpectedly on new data.

Core claim

The method identifies a small set of spurious-positive samples to locate highly relevant neurons in an intermediate layer, which are then regularized to prevent the model from depending on spurious attributes for predictions, ensuring reliance on core informative features and enhancing robustness.

What carries the argument

Targeted identification of spurious-positive instances followed by neuron regularization in an intermediate layer, based on reasoning that certain features should not influence predictions.

Load-bearing premise

A small set of instances can be reliably identified as those where the model relies on spurious attributes, and regularizing the corresponding neurons will reduce shortcut dependence without harming core performance.

What would settle it

Observing that regularization of the identified neurons either fails to reduce spurious dependence or decreases accuracy on the main task would falsify the central claim.

Figures

Figures reproduced from arXiv: 2605.13340 by Christin Seifert, Gemma Roig, J\"org Schl\"otterer, Phuong Quynh Le, Sari Sadiya.

**Figure 1.** Figure 1: Overview of SCORE. A. For each instance, we compute activations and LRP masks. Among the most activated samples per class, we select spurious-positive samples using the masked images. B. For these samples, we extract activation vectors from the original inputs and shortcut relevance vectors from the masked images. We apply ReLU to the shortcut relevance vectors and combine them element-wise with the activa… view at source ↗

**Figure 2.** Figure 2: Selected LRP masks for shortcut-positive samples. For each dataset, we show two example pairs of masked and original image (mi, xi) used for regularization, the spurious attribute s, and the class which s is spurious-positive for. From 100 analyzed samples, we retain t samples that clearly highlight spurious features; t being specific for each dataset. a) core core & spurious spurious b) c) Regularization … view at source ↗

**Figure 3.** Figure 3: a) Masking based on LRP activations shows that the model uses spurious features. b) Most strongly activated neurons for spurious-positive samples encode spurious semantic concepts. (c) SCORE penalizes reliance on spurious-activating neurons, forcing the model to learn informative features. 4.3 Results for Mitigation Quantitative Results. We report worst-group accuracy (WGA) and average accuracy (AVG) for a… view at source ↗

**Figure 4.** Figure 4: We show UMAP embeddings with highlighting the activating image regions of the top-50 benign samples, which strongly activate the prediction in both the baseline model (left) and after fine-tuning (right). After fine-tuning, the color patches are no longer the most representative features for the target class. File: temp__0011481 File: temp__0011481 File: temp__0010777 File: temp__0010777 File: temp__000866… view at source ↗

**Figure 5.** Figure 5: LRP heatmaps for samples from the baseline model (top) and after fine-tuning using SCORE (bottom). The results show that the proposed regularization effectively eliminates the reliance on spurious features (color patches, rulers, hairs). To assess class-relevant features after fine-tuning, we apply LRP to the most activating samples per class [PITH_FULL_IMAGE:figures/full_fig_p012_5.png] view at source ↗

**Figure 6.** Figure 6: Left: 2D-UMAP visualization of the Knee dataset showing the most relevant image patches for class predictions. Many highlighted regions correspond to artificially inserted patches, indicating the model’s reliance on spurious features. Right: 2D-UMAP visualization shows the most relevant image patches for the healthy class; lighter regions are more important. Waterbird100 dataset [PITH_FULL_IMAGE:figures/f… view at source ↗

**Figure 7.** Figure 7: 2D-UMAP visualization of the top-25 samples strongly activate the prediction of the class landbird (left) and waterbird (right) from the dataset Waterbird-100. #0 #1 all 1 2 Block3_#6 [PITH_FULL_IMAGE:figures/full_fig_p022_7.png] view at source ↗

**Figure 8.** Figure 8: Top-50 samples strongly activate the prediction of class waterbird (left) from the dataset WB100. Semantic meanings of spurious neurons (right). ISIC Dataset. For ISIC dataset, we analyze several cases of top-m samples activating the benign class [PITH_FULL_IMAGE:figures/full_fig_p022_8.png] view at source ↗

**Figure 9.** Figure 9: Top-m samples strongly activate the prediction of the class benign in ISIC dataset. We select m = 50 (left) and m = 25 (right). We can observe the spurious features (patches) easily, even in the top few samples activating the class [PITH_FULL_IMAGE:figures/full_fig_p022_9.png] view at source ↗

**Figure 10.** Figure 10: 2D UMAP embedding of neurons strongly activating for spurious features. D Vision Transformers Extending SCORE to ViT, we replace the original LRP by AttnLRP [1]. Qualitative results show that Attn-LRP successfully highlights class-associated spurious features ( [PITH_FULL_IMAGE:figures/full_fig_p023_10.png] view at source ↗

**Figure 11.** Figure 11: Detected spurious features (left-to-right): land background (WB95), water background (WB95), patches (ISIC), markers (Knee) [PITH_FULL_IMAGE:figures/full_fig_p023_11.png] view at source ↗

read the original abstract

Shortcut mitigation strategies commonly rely on training data annotations, group-balanced held-out data or the presence of all groups, i.e., all combinations of (spurious) attributes and classes, in the training data. However, these requirements are rarely met in practice. We instead propose a method for targeted model analysis to identify a small set of instances in which the model relies on spurious attributes. Using that set and following ``this feature should not be used for prediction'' reasoning, we identify highly relevant neurons in an intermediate layer and regularize their impact. This ensures that models learn to depend on informative features rather than being right for the wrong reasons, thereby improving robustness without requiring additional balanced held-out data or annotations.

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

The paper outlines a no-annotation shortcut fix that spots spurious-positive instances then regularizes key neurons, but the high-level description leaves performance claims untested.

read the letter

The main takeaway is a method that picks out a small set of training examples where the model appears to use spurious attributes, then identifies relevant neurons in an intermediate layer and regularizes them to reduce that dependence. It targets the common case where you have neither spurious labels nor balanced held-out data. That framing matches a real deployment gap. The targeted regularization step gives a concrete way to operationalize avoiding the wrong features without retraining everything or adding new data sources. The specific pipeline of instance selection followed by neuron-level intervention is not exactly in the prior shortcut literature, so there is incremental novelty in how the pieces are combined. It builds sensibly on existing ideas around spurious sample analysis and feature regularization. The central assumption that a small set of instances can surface the right neurons without harming core performance is stated plainly and avoids obvious circularity. The soft spot is that the abstract stays high-level with no walk-through of the identification rules, no derivation of the regularization term, and no reported numbers on robustness gains or accuracy trade-offs. Without those, it is difficult to judge whether the neuron step actually delivers the claimed improvement or just shifts the shortcut elsewhere. The method looks feasible on its own terms but needs the experiments to show it holds up. This is the sort of work that would interest people building robust models on messy real datasets where annotations are expensive. A reader looking for practical tweaks to try on their own data could get value from the pipeline even if it requires tuning. I would send it to referees. The problem is relevant and the proposal is clear enough to get useful feedback.

Referee Report

2 major / 1 minor

Summary. The paper proposes a shortcut mitigation method that uses targeted model analysis to identify a small set of spurious-positive instances, locates highly relevant neurons in an intermediate layer, and applies regularization to those neurons so that the model relies on informative rather than spurious features. The approach is presented as requiring neither annotations nor group-balanced held-out data.

Significance. If the empirical claims hold, the method would address a practical gap in shortcut mitigation, since many existing techniques depend on resources that are often unavailable. The core idea of using internal model analysis for instance selection and neuron regularization is a potentially useful direction for improving robustness in standard training pipelines.

major comments (2)

[Abstract] Abstract: the central claim that the method 'improves robustness' is stated without any reference to datasets, baselines, or quantitative metrics; this absence is load-bearing because the soundness of the approach cannot be evaluated from the high-level pipeline alone.
[Method] Method description: the criteria for selecting the 'small set of instances' and for identifying 'highly relevant neurons' are described only at a conceptual level; without explicit algorithms, thresholds, or ablation results, it is unclear whether the procedure is stable across random seeds or model architectures.

minor comments (1)

[Abstract] The phrase 'this feature should not be used for prediction' is used without a formal definition or reference to prior work on feature attribution; a short clarification would improve readability.

Circularity Check

0 steps flagged

No significant circularity detected in derivation chain

full rationale

The paper's core pipeline—identifying a small set of spurious-positive instances through model-internal analysis, locating relevant neurons in an intermediate layer, and applying regularization—does not reduce to its inputs by construction. No equations, fitted parameters renamed as predictions, or self-citations are invoked in the provided text to create a self-definitional loop or load-bearing dependency. The method is framed as relying on internal model properties rather than external annotations or balanced data, and the derivation remains independent without renaming known results or smuggling ansatzes via prior work. This is the expected honest non-finding for a method whose central steps are presented as externally verifiable through model inspection.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

No explicit free parameters, axioms, or invented entities are stated in the abstract; the approach implicitly assumes that spurious reliance can be localized to specific neurons without additional supervision.

pith-pipeline@v0.9.0 · 5425 in / 945 out tokens · 23934 ms · 2026-05-14T19:10:11.829793+00:00 · methodology

discussion (0)

Reference graph

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