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REVIEW 4 major objections 6 minor 96 references

Towards Utilising a Range of Neural Activations for Comprehending Representational Associations

T0 review · 4 major / 6 minor · reviewed 2026-08-12 · deepseek-v4-flash

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Pith's one-line read This paper claims that the samples a classifier is least confident about—those with near-zero logits—concentrate the examples that break spurious correlations, and that retraining the classifier's last layer on a clustered subset of those…

desk verdict A promising no-group-label method for finding spurious correlations via mid-level logits, with strong WGA gains but an unmeasured core mechanism and a human-in-the-loop cluster selection that deserves scrutiny. read the letter →

arxiv 2411.10019 v1 pith:WUXFKXDT submitted 2024-11-15 cs.CV cs.LG

Laura O'Mahony , Nikola S. Nikolov , David JP O'Sullivan This is my paper · ORCID
classification cs.CVcs.LG MSC 68T0768T45
keywords mid-levelactivationsspuriouscorrelationsworst-groupaccuracyinterpretabilitylast-layerretrainingmid-rangelogitsconceptentanglementlogitinterceptdata
verification ladder T0 review T1 audit T2 compute T3 formal

The pith

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

The reading

Interpretability research has mostly asked which inputs maximally activate a neuron or concept direction, implicitly treating activation as binary. This paper argues that the unexamined middle of the activation range carries the signal that matters: samples with near-zero logits for a class are disproportionately either in-class examples that lack the spurious attribute the model relies on, or out-of-class examples that contain it. The authors turn this observation into a practical method, MID (Modifying with logit Intercept Data): select mid-range logit samples, filter mislabels, cluster their frozen penultimate embeddings, identify the cluster whose poor classification is inappropriate (a spurious pattern), and retrain only the classification layer on that cluster. On the Waterbirds and CelebA benchmarks this lifts worst-group accuracy from 68.4 and 47.8 under standard training to 87.8 and 85.5, matching or beating methods that require group labels. The paper's broader claim is that non-extremal activations are a general resource for locating entangled and confounding concepts in representations.

What carries the argument

The load-bearing object is the 'intercept data': the set of training samples whose logit for a chosen class lies near zero, where the model's prediction confidence is lowest. The paper argues that this band is enriched for two kinds of examples that break the spurious trend—in-class samples lacking the spurious attribute and out-of-class samples carrying it—and that k-means clustering of their penultimate-layer embeddings separates a coherent group whose poor accuracy is caused by the spurious association rather than by benign ambiguity. Carrying the argument further, the last layer of the ERM model is replaced and retrained by $\ell^1$-regularised logistic regression on this selected cluster, exploiting the finding that ERM still learns core features and only the final mapping needs correction.

What would settle it

Measure, on CelebA or Waterbirds, the density of minority-group samples (blonde males, or water birds on land backgrounds) inside the near-zero-logit band selected by MID; if it is no higher than the base rate in the training set, the mid-range selection does not target spurious correlations and the method's mechanism is wrong. A second check: retrain the last layer on a randomly chosen cluster instead of the cluster judged to encode the spurious pattern—the paper's own ablation shows random-cluster retraining does not reliably improve worst-group accuracy, so a reader could verify this holds across seeds and datasets.

Watch

Extended reading notes

Core claim

The paper's central claim is that intermediate representations encode information at every level of stimulation, not just at the extremes, and that the mid-range of output-neuron activations is where a model's learned associations between class labels and confounding attributes become visible. Concretely, when images are ranked by their logit for a class, the maximally activating examples are typical of the majority spurious trend, while examples near the zero-logit intercept include shapes in a spurious location for the wrong class, low-spuriosity instances of the class, and mislabels—associations that maximum-activation analysis hides. MID operationalises this: for each class it keeps the near-zero logit samples, removes disputed labels using a vision-language model, clusters the encoder embeddings with k-means, and selects the cluster whose poor performance cannot be excused by image quality or labelling ambiguity (for CelebA, blonde faces with masculine features). Retraining the final layer on that cluster with $\ell^1$-regularised logistic regression, on top of the frozen feature extractor, improves worst-group accuracy to 87.8 on Waterbirds and 85.5 on CelebA without access to spurious-attribute labels for training or validation. The same logic, the paper argues, extends beyond output neurons to any concept direction in latent space found by methods such as TCAV or SVD-based decomposition.

Load-bearing premise

The method assumes that near-zero-logit samples are enriched for examples that break the spurious trend rather than dominated by mislabels, label noise, or low-quality images, and that this enrichment, demonstrated on synthetic DSpritesUnfair data, transfers to CelebA and Waterbirds.

Editorial extensions

If this is right

  • Maximal-activation interpretability is incomplete: analyses that only inspect the highest-activating examples will systematically miss the associations and confounds a model encodes at mid-range stimulation.
  • Last-layer retraining on automatically selected mid-logit clusters can match or approach the worst-group accuracy of methods that require group labels, while needing none for training or validation.
  • The pipeline is cheap: it reuses the frozen penultimate features of a standard ERM model and only re-fits the final layer with logistic regression.
  • The same mid-range inspection applies to any concept direction in latent space, not just output neurons, so confounding concepts found by concept-vector methods could be audited and mitigated the same way.

Reading between the lines

Editorial extensions of the paper, not claims the author makes directly.

  • A natural extension the paper does not test: iterate MID—retrain, re-select the mid-range band, and re-cluster—to see whether repeated rounds keep removing spurious reliance or converge to a stable selection.
  • The mid-logit band is a free, unsupervised proxy for the samples a model has memorised against its own trend; the same filter could be reused to flag out-of-distribution inputs or label noise in other benchmarks, since the paper shows it captures 55.1% of the ERM model's errors on CelebA.
  • Because the cluster-interpretation step is the only human-in-the-loop part, automating it with a vision-language model (which the paper mentions but does not evaluate) is a direct testable extension.
  • The near-zero-logit heuristic suggests a broader principle: for any neuron or direction, the decision boundary in activation space may be where entangled concepts live, connecting this work to geometric accounts of polysemanticity.
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Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper argues that interpretability analyses based on maximally activating examples miss information encoded at non-maximal activation levels, and proposes a practical method, MID (Modifying with logit Intercept Data). MID first trains a standard ERM classifier, then selects samples whose class logits are near zero (the logit 'intercept'), filters suspected mislabels with BLIP, clusters the penultimate-layer embeddings of the remaining samples with k-means, manually selects a cluster that exhibits a harmful spurious pattern, and retrains the classification layer using DFR-style logistic regression. On Waterbirds and CelebA, MID reports worst-group accuracies of 87.8 and 85.5, respectively, compared with ERM baselines of 68.4 and 47.8, and is competitive with methods that use group labels while requiring none.

Significance. If the mechanism claimed by the paper holds, this is a valuable bridge between interpretability and robustness: it would show that non-maximal activations can expose confounds without group labels, and the proposed last-layer retraining is simple and cheap. The paper is honest about the human-in-the-loop nature of the method and provides ablations suggesting that both the mid-logit filter and the choice of cluster matter. The reported WGA gains are large, and the use of DFR-style retraining is well motivated by prior work. However, the central enrichment assumption -- that near-zero logit samples are enriched for low-spuriosity and counter-spurious examples -- is only qualitatively demonstrated on a synthetic dataset and never directly measured on the real benchmarks, so the significance of the empirical results is conditional on that assumption being validated.

major comments (4)
  1. [Sec. 4.2, Fig. 3 and Sec. 5.1] The core claim that near-zero class logits are enriched for low-spuriosity and counter-spurious examples is supported only by a qualitative inspection of six images on DSpritesUnfair (bias 0.4). No quantitative enrichment statistic is reported for Waterbirds or CelebA, and Fig. 10 shows the group composition only of the final cluster, which is already the product of human selection of k and of the 'spurious' cluster. Because the Table 1 gains could in principle arise from DFR-style retraining on any hard or balanced subset rather than from a property specific to mid-level activations, the paper should measure the group composition of the mid-logit selection before clustering (e.g., the fraction of minority-group examples among selected samples versus random samples matched by logit rank) and report this for both benchmarks.
  2. [Sec. 5.1 and Appendix A.6] The cluster selection step is not prespecified: k is increased 'until a pattern emerges', and the cluster used for retraining is chosen by manual inspection of cluster contents. For CelebA this selects the cluster corresponding to the known spurious attribute (blonde with masculine-associated features). This introduces human selection based on prior knowledge of each benchmark's known bias and undercuts the claim of operating without group information. Please specify a preselectable or automated rule for choosing k and the retraining cluster, and report sensitivity to both choices (e.g., WGA for every cluster and every k tried).
  3. [Appendix A.7] The ablation of the mid-logit filter is qualitative only. The paper states that clustering the full CelebA embeddings did not reveal a spurious pattern and that retraining on one ambiguous cluster does not help WGA, but no quantitative worst-group accuracies are given for these ablations. A quantitative comparison of MID against (a) retraining on a random balanced subset, (b) retraining on BLIP-filtered mid-logit data without clustering, and (c) retraining on each individual k-means cluster would isolate the contribution of the mid-logit selection and the cluster choice. This is needed to support the claim that the mid-level activation lens, rather than the last-layer retraining mechanism, is responsible for the improvements.
  4. [Sec. 5.1 and Appendix A.5] The mid-range selection rule is underspecified. For CelebA the paper reports 2,000 logit-intercept points per class, but for Waterbirds no analogous count or threshold is given, and the exact definition of 'near-zero logit' is not operationalized (e.g., a window around zero, a fixed number per class, or a sign-change region). Without a precise selection rule the method cannot be reproduced or compared across datasets; please specify the selection criterion and report the number of selected points for both benchmarks.
minor comments (6)
  1. [Table 1] The entry 'DFRTrTr' is not defined in the table or caption; the text in Sec. 6.3 mentions 'DFR T r T r' but it should be spelled out as a specific DFR variant.
  2. [Sec. 6.3 and Table 1] No variance or number of seeds is reported for the Waterbirds and CelebA ERM and MID runs, despite the DSprites pilot reporting means over five seeds. Since Table 1 is the main quantitative claim, please include standard deviations or error bars over multiple initializations.
  3. [Appendix A.6] The sentence 'we train on the available validation data with additional randomly selected training set data' is ambiguous and appears to contradict Sec. 6.4's statement that group information is used only for evaluation. Please clarify whether unlabeled validation data are used during retraining and how this affects the comparison with baselines that use group-balanced validation sets.
  4. [Sec. 7] The claim that points 'right above' the zero logit are more likely to be low-spuriosity memorized points while points 'right below' are more likely to be counter-spurious examples is presented as a finding but is not measured anywhere in the paper; it should be framed as a hypothesis or supported with data.
  5. [Table 2 and References] There is a LaTeX artifact in Table 2 ('L n(L)'), and several reference formatting issues such as 'Hermann et Lampinen' and 'TCA V' with inconsistent spacing; these should be cleaned up.
  6. [Figs. 3 and 6] The example images in Figs. 3 and 6 are very small, making the qualitative claims about position and shape difficult to verify; larger crops or additional annotations would help.

Circularity Check

0 steps flagged · score 0.0 of 10

No significant circularity: MID's WGA improvements are measured on held-out benchmark test sets, and the paper's assumptions, while empirically under-supported, are not definitionally tied to its outputs.

full rationale

The paper's central claim is empirical: mid-range logit samples, after k-means clustering and human selection of a 'spurious' cluster, can be used to retrain the classification layer and improve worst-group accuracy on Waterbirds and CelebA. These improvements (Table 1) are evaluated on external held-out test sets with group labels used only for evaluation, so they do not reduce by construction to the selection procedure. The key assumption that near-zero logits are enriched for low-spuriosity and counter-spurious samples is supported only qualitatively on DSpritesUnfair and is not directly measured on the real benchmarks; this is a validity and evidential weakness, not a circularity. The human-in-the-loop choice of k and cluster, and the tuning of the regularization strength on a split of the selected retraining data, are explicitly disclosed (Sec. 5.1, Sec. 6.3, Appendix A.6) and do not constitute a fitted parameter being renamed as a prediction. The last-layer retraining step is borrowed from DFR [39] with citation, and the paper does not claim that step as a novel derivation. Self-citations in the related work are not load-bearing for the main result. No equation or definition in the paper makes the reported WGA equal to an input by construction.

Assumptions & free parameters 4 free parameters · 4 assumptions · 0 invented entities

The method introduces no new physical or mathematical entities. The main load-bearing elements are the free parameters of the data-selection and clustering pipeline (number of mid-logit samples, number of clusters, manual cluster choice, regularization strength) and the domain assumptions about last-layer retraining and the informativeness of mid-level logits. These assumptions are empirically motivated but not proven.

free parameters (4)
  • Mid-range logit count per class = 2000 for CelebA; unspecified for Waterbirds
    The number of samples taken near the zero-logit intercept per class is set by the user; the paper uses 2000 for CelebA and does not report the value for Waterbirds, so the threshold is a free parameter.
  • Number of clusters k = 3 for CelebA (k=2..4 explored)
    The paper starts at k=2 and increases until a pattern emerges; k=3 is selected for CelebA. This hand-chosen parameter affects which grouping is later used for retraining.
  • Spurious cluster selection = Cluster judged to reflect an unacceptable spurious pattern (e.g., masculine-featured blondes for CelebA)
    After k-means, a human inspects cluster contents and decides which cluster to use for retraining. This choice is not determined by a fixed rule and depends on the dataset and human judgment.
  • Regularization strength of logistic regression = 1.0 from {1.0, 0.7, 0.3, 0.1, 0.07, 0.03, 0.01} for both datasets
    Tuned on one half of the selected retraining data, so it is fit to data rather than set a priori.
assumptions (4)
  • domain assumption Last-layer retraining is sufficient to reduce spurious feature reliance
    The method assumes that the frozen penultimate-layer embeddings retain core features, so retraining only the classification layer can drop spurious features. This is borrowed from Kirichenko et al. (DFR) and Izmailov et al. and is invoked in Sec 5.1 and Step 4.
  • domain assumption BLIP VQA can reliably identify mislabeled or ambiguous hair-color images
    The filter step uses BLIP in a VQA setting to remove disputed labels; the paper assumes the model's answers are accurate enough for this curation, which is not independently verified.
  • ad hoc to paper Near-zero logit intercept sets are enriched for low-spuriosity and counter-spurious examples
    The central hypothesis of the paper. It is demonstrated empirically on one synthetic setting (DSpritesUnfair, bias 0.4) and assumed to transfer to CelebA and Waterbirds without a formal justification.
  • domain assumption Frozen penultimate-layer embeddings preserve core features necessary for last-layer retraining
    The paper relies on the prior finding that ERM models learn core features despite relying on spurious shortcuts, so that the last layer alone can be retrained. This is cited from Kirichenko et al. and Izmailov et al.

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Cite this review

Pith. "Pith review of Towards Utilising a Range of Neural Activations for Comprehending Representational Associations." pith.science (2026). https://pith.science/paper/WUXFKXDT

@misc{pith2026241110019,
  author       = {Pith},
  title        = {Pith review of: Towards Utilising a Range of Neural Activations for Comprehending Representational Associations},
  year         = {2026},
  howpublished = {\url{https://pith.science/paper/WUXFKXDT}},
  note         = {Machine review of arXiv:2411.10019}
}
read the original abstract

Recent efforts to understand intermediate representations in deep neural networks have commonly attempted to label individual neurons and combinations of neurons that make up linear directions in the latent space by examining extremal neuron activations and the highest direction projections. In this paper, we show that this approach, although yielding a good approximation for many purposes, fails to capture valuable information about the behaviour of a representation. Neural network activations are generally dense, and so a more complex, but realistic scenario is that linear directions encode information at various levels of stimulation. We hypothesise that non-extremal level activations contain complex information worth investigating, such as statistical associations, and thus may be used to locate confounding human interpretable concepts. We explore the value of studying a range of neuron activations by taking the case of mid-level output neuron activations and demonstrate on a synthetic dataset how they can inform us about aspects of representations in the penultimate layer not evident through analysing maximal activations alone. We use our findings to develop a method to curate data from mid-range logit samples for retraining to mitigate spurious correlations, or confounding concepts in the penultimate layer, on real benchmark datasets. The success of our method exemplifies the utility of inspecting non-maximal activations to extract complex relationships learned by models.

Figures

Figures reproduced from arXiv: 2411.10019 by the authors.

Figure 1
Figure 1. An illustration of the spurious correlation data analysis and automatic selection for retraining to mitigate the found spurious bias [PITH_FULL_IMAGE:figures/full_fig_p002_1.png] view at source ↗
Figure 3
Figure 3. (a) Maximally activating examples for the neuron corre [PITH_FULL_IMAGE:figures/full_fig_p005_3.png] view at source ↗
Figure 4
Figure 4. UMAP projections for encoder embeddings of (a) all [PITH_FULL_IMAGE:figures/full_fig_p008_4.png] view at source ↗
Figures from the paper (7 more)
Figure 5
Figure 5. Figure 5: Representational similarity matrices plotted for encoders trained with varying levels of bias (below each image). The order of [PITH_FULL_IMAGE:figures/full_fig_p015_5.png]
Figure 6
Figure 6. Figure 6: (a) Maximally activating examples for the neuron corre [PITH_FULL_IMAGE:figures/full_fig_p015_6.png]
Figure 7
Figure 7. Figure 7: Logits in descending order for the class non-blonde (in [PITH_FULL_IMAGE:figures/full_fig_p016_7.png]
Figure 8
Figure 8. Figure 8: Middle logit images with y labels on top disputed by BLIP labels shown below each image. [PITH_FULL_IMAGE:figures/full_fig_p017_8.png]
Figure 9
Figure 9. Figure 9: Group composition of k-means clusters for [PITH_FULL_IMAGE:figures/full_fig_p017_9.png]
Figure 10
Figure 10. Figure 10: Group composition of k-means clustering on the fil [PITH_FULL_IMAGE:figures/full_fig_p018_10.png]
Figure 11
Figure 11. Figure 11: Group composition of k-means clustering on the entire [PITH_FULL_IMAGE:figures/full_fig_p018_11.png]

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Pith tools

Reviewed August 12, 2026 · model on record in the stance chip above.