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arxiv: 2605.10165 · v2 · pith:XPDYHPWSnew · submitted 2026-05-11 · 💻 cs.CV · cs.AI

Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation

Inhyuk Park , Doohyun Park This is my paper

Pith reviewed 2026-05-21 08:23 UTC · model grok-4.3

classification 💻 cs.CV cs.AI
keywords noisy label detectionmedical imagingcross-validationloss aggregationfundus imagestask-agnostic
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The pith

SLA detects noisy labels by aggregating standardized validation losses from repeated cross-validation into continuous scores.

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

The paper proposes Standardized Loss Aggregation (SLA) to find noisy labels in large datasets like those in medical imaging. SLA works by standardizing the validation losses from each fold in repeated cross-validation runs and then aggregating them. This turns the idea of counting how often a sample performs poorly into a continuous score that also considers how bad the performance was. The result is more stable scores that better identify unreliable labels compared to simpler methods, as shown in experiments on a fundus image dataset.

Core claim

SLA quantifies label reliability by aggregating standardized fold-level validation losses across repeated cross-validation runs. This formulation generalizes discrete hard-counting schemes into a continuous estimator that captures both the frequency and magnitude of performance deviations, yielding interpretable and statistically stable noisiness scores.

What carries the argument

Standardized Loss Aggregation (SLA): a method that standardizes and aggregates fold-level validation losses from multiple cross-validation iterations to produce a sample-level noisiness score.

If this is right

  • Outperforms hard-counting baseline across all noise levels on fundus datasets.
  • Converges faster especially under low noise ratios where subtle variations matter.
  • High scores indicate ambiguous or mislabeled cases suitable for re-annotation.
  • Enhances dataset reliability for any classification task by guiding efficient cleaning.

Where Pith is reading between the lines

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

  • Applying SLA to other noisy label scenarios in non-medical data could reveal similar benefits.
  • Integrating SLA scores into training loops might allow dynamic sample weighting beyond just detection.
  • Testing SLA with different model architectures would show if the scores are truly task-agnostic as claimed.

Load-bearing premise

Standardized validation losses from cross-validation folds indicate label noise rather than model capacity, data distribution shifts, or other factors.

What would settle it

An experiment injecting known label noise into a clean dataset and verifying whether SLA assigns high scores specifically to the noisy samples would confirm or refute the method.

read the original abstract

Noisy labels are common in large-scale medical imaging datasets due to inter-observer variability and ambiguous cases. We propose a statistically grounded and task-agnostic framework, Standardized Loss Aggregation (SLA), for detecting noisy labels at the sample level. SLA quantifies label reliability by aggregating standardized fold-level validation losses across repeated cross-validation runs. This formulation generalizes discrete hard-counting schemes into a continuous estimator that captures both the frequency and magnitude of performance deviations, yielding interpretable and statistically stable noisiness scores. Experiments on a public fundus dataset demonstrate that SLA consistently outperforms the hard-counting baseline across all noise levels and converges substantially faster, especially under low noise ratios where subtle loss variations are informative. Samples with high SLA scores indicate potentially ambiguous or mislabeled cases, guiding efficient re-annotation and improving dataset reliability for any classification task.

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 / 2 minor

Summary. The manuscript proposes Standardized Loss Aggregation (SLA) as a statistically grounded, task-agnostic method for sample-level noisy label detection. SLA aggregates standardized fold-level validation losses across repeated cross-validation runs to produce a continuous noisiness score that generalizes discrete hard-counting schemes by capturing both frequency and magnitude of performance deviations. Experiments on a public fundus dataset are reported to show consistent outperformance over hard-counting baselines across noise levels with substantially faster convergence, especially at low noise ratios.

Significance. If the experimental claims are substantiated with quantitative detail and controls, SLA could provide a practical continuous estimator for identifying ambiguous or mislabeled samples in medical imaging datasets, improving downstream classification reliability. The generalization from hard-counting and emphasis on statistical stability are conceptually appealing strengths. However, the current presentation leaves the significance limited by unverified quantitative assertions and unresolved questions about whether the scores isolate label noise.

major comments (2)
  1. Abstract: the central claim of consistent outperformance and faster convergence on the public fundus dataset is stated without any quantitative results, error bars, tables, or specifics on the standardization procedure or number of CV repetitions; this renders the experimental support for the method unverified and load-bearing for the paper's contribution.
  2. Method section (description of SLA construction): the standardization of per-fold validation losses is presented as removing confounding effects, yet the same model family is trained directly on the noisy labels; no controls (e.g., difficulty-matched clean subsets, architecture ablations, or comparison to loss on clean data) are described to separate label noise from inherent sample difficulty or model capacity, undermining the task-agnostic and noise-specific interpretation.
minor comments (2)
  1. Add explicit equations or pseudocode for the standardization step and aggregation formula to clarify how the continuous score is computed from fold losses.
  2. The abstract mentions 'interpretable and statistically stable noisiness scores' but provides no discussion of variance across runs or statistical tests; consider adding this in the results section.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive comments, which have helped clarify the presentation of our work. We address each major comment below and have revised the manuscript to incorporate the suggested improvements.

read point-by-point responses

  1. Referee: Abstract: the central claim of consistent outperformance and faster convergence on the public fundus dataset is stated without any quantitative results, error bars, tables, or specifics on the standardization procedure or number of CV repetitions; this renders the experimental support for the method unverified and load-bearing for the paper's contribution.

    Authors: We agree that the abstract would benefit from quantitative support to make the claims verifiable. In the revised version we have added specific metrics (including mean AUC improvement and convergence iterations with standard deviations across runs), the number of cross-validation repetitions (five repetitions of five-fold CV), and a concise description of the per-fold z-score standardization procedure. revision: yes

  2. Referee: Method section (description of SLA construction): the standardization of per-fold validation losses is presented as removing confounding effects, yet the same model family is trained directly on the noisy labels; no controls (e.g., difficulty-matched clean subsets, architecture ablations, or comparison to loss on clean data) are described to separate label noise from inherent sample difficulty or model capacity, undermining the task-agnostic and noise-specific interpretation.

    Authors: We acknowledge that explicit controls would strengthen the claim that SLA isolates label noise rather than sample difficulty. The standardization normalizes losses within each fold to reduce scale differences, but we recognize this alone does not fully rule out difficulty confounds. We have therefore added an experiment comparing SLA scores on clean versus noise-injected samples and an architecture ablation study; these results are now reported in the revised experiments section to support the noise-specific interpretation. revision: yes

Circularity Check

0 steps flagged

No significant circularity; SLA is a direct definition from external CV losses

full rationale

The paper defines SLA explicitly as the aggregation of standardized fold-level validation losses obtained from repeated cross-validation runs on the given dataset. These losses are treated as independent measurements produced by training, not as quantities derived from the noisiness score itself. No equation or step reduces the final noisiness score back to a fitted parameter, a self-referential definition, or a load-bearing self-citation whose validity depends on the present work. The generalization from hard-counting is presented as a reformulation of the same external loss observations rather than a derivation that presupposes its own output. The central claim therefore remains self-contained against the cross-validation procedure and does not exhibit any of the enumerated circular patterns.

Axiom & Free-Parameter Ledger

0 free parameters · 1 axioms · 0 invented entities

With only the abstract available, the ledger is necessarily incomplete. The central claim rests on the unstated premise that cross-validation losses can be standardized in a way that isolates label noise from other sources of variation.

axioms (1)
  • domain assumption Validation losses from cross-validation folds are a valid proxy for label reliability independent of model architecture and task.
    Invoked when SLA is positioned as task-agnostic and statistically grounded.

pith-pipeline@v0.9.0 · 5666 in / 1282 out tokens · 40867 ms · 2026-05-21T08:23:38.066065+00:00 · methodology

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Reference graph

Works this paper leans on

24 extracted references · 24 canonical work pages · 1 internal anchor

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    Task-Agnostic Noisy Label Detection via Standardized Loss Aggregation

    INTRODUCTION Deep learning has achieved remarkable progress in computer vision and medical image analysis. However, its performance critically depends on large-scale datasets with reliable annotations . In clinical imaging, obtaining such annotations is both costly and challenging: expert labeling is time-consuming and often inconsistent due to inter- obs...

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    METHOD 2.1. Overview The proposed framework, calledStandardized Loss Aggregation (SLA), estimates sample-level label noisiness by aggregating stan- dardized fold-level validation losses across repeated K-fold cross- validation. Unlike discrete count-based methods that only record occurrences in the worst-performing folds, SLA continuously inte- grates nor...

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