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arxiv: 2606.08718 · v1 · pith:ZCT3GCTQnew · submitted 2026-06-07 · 💻 cs.LG · cs.AI

Deep Active Re-Labeling: Toward Noise-Resilient Annotation Efficiency

Md Abdullah Al Forhad , Weishi Shi This is my paper

Pith reviewed 2026-06-27 18:24 UTC · model grok-4.3

classification 💻 cs.LG cs.AI
keywords deep active learningannotation noisere-labelingdata efficiencynoise detection
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The pith

Allocating annotation budget to re-label noisy data makes deep active learning more efficient and noise-resilient.

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

Human annotation errors on the samples chosen by active learning can make performance worse than passive learning. The proposed method uses the model to find likely noisy labels and spends some budget re-annotating them. Experiments show this produces a cleaner dataset and better models for the same total annotation effort.

Core claim

By implementing active noise sampling to select instances for re-annotation, the framework removes noise from the active training set and achieves higher data efficiency under fixed annotation budgets.

What carries the argument

Active noise sampling strategies that detect potentially noisy labels for re-annotation.

If this is right

  • Active learning regains its performance advantage over passive learning despite annotation noise.
  • The final training set contains fewer errors.
  • Models reach higher accuracy without increasing the overall labeling cost.

Where Pith is reading between the lines

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

  • The re-labeling idea could extend to semi-supervised settings where pseudo-labels might be noisy.
  • Combining this with robust loss functions might further improve results.
  • Applying it to image, text, or other modalities would test its generality.

Load-bearing premise

The model has enough ability to identify which data points have noisy labels.

What would settle it

A test showing that re-annotating the selected noisy samples does not reduce the error rate or improve final model accuracy compared to not re-labeling.

Figures

Figures reproduced from arXiv: 2606.08718 by Md Abdullah Al Forhad, Weishi Shi.

Figure 1
Figure 1. Figure 1: (a) Analyzing the consequences of deep active sampling [PITH_FULL_IMAGE:figures/full_fig_p001_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Relationship between re-labeling count and incorrect [PITH_FULL_IMAGE:figures/full_fig_p003_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Our proposed framework for deep active re-labeling in DAL setting. [PITH_FULL_IMAGE:figures/full_fig_p004_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Different types of noisy annotations. (a) A noisy [PITH_FULL_IMAGE:figures/full_fig_p005_4.png] view at source ↗
Figure 5
Figure 5. Figure 5: Results of the re-labeling strategy applied to (a) MNIST, (b) FashionMNIST, (c) CIFAR-10, and (d) MedMNIST. [PITH_FULL_IMAGE:figures/full_fig_p007_5.png] view at source ↗
Figure 6
Figure 6. Figure 6: (a-d): Impact of hyperparameters. The impact of hyperparameter [PITH_FULL_IMAGE:figures/full_fig_p008_6.png] view at source ↗
Figure 7
Figure 7. Figure 7: Comparison between the performance of two different [PITH_FULL_IMAGE:figures/full_fig_p009_7.png] view at source ↗
read the original abstract

While Deep Active Learning (DAL) effectively reduces human annotation costs, its efficacy is constrained by human annotation errors. This is because the data sampled for active learning is assumed to be highly informative for training. When human annotators introduce errors into this informative data at a certain rate, the active learning performance drops significantly and, in some cases, even exhibits worse outcomes than passive learning. In this paper, we first analyze the impact of human annotation errors in the DAL setting. Then we propose a framework to address the human annotation noise problem for DAL. Informed by human learning patterns, the core idea of our proposed solution involves allocating a portion of the human annotation budget to re-annotate data that has already been labeled. Previous theoretical work suggests that when the model possesses a certain level of ability to identify potentially noisy data, even re-labeling a small fraction of the data can effectively remove noise from the active training set. To achieve this, we implement two active noise sampling strategies to detect noise under different circumstances and allocate a part of the annotation budget to re-annotate these instances. Our approach imbues active learning with a revisiting and introspective behavior. Our experiments demonstrate that, under the same annotation budget, our method is more data-efficient and yields a relatively noise-free annotation dataset in the end.

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 / 1 minor

Summary. The paper analyzes the negative impact of human annotation noise on deep active learning (DAL) performance and proposes Deep Active Re-Labeling, a framework that reserves part of a fixed annotation budget for re-annotating instances flagged as noisy by two active noise sampling strategies. Drawing on prior theory, it posits that re-labeling a small fraction suffices once the model reaches a sufficient (unspecified) noise-identification accuracy, yielding a more data-efficient and relatively noise-free training set.

Significance. If the empirical gains hold and the noise-identification precondition is met, the work would offer a practical, budget-neutral way to improve DAL robustness to label noise. The introspective re-labeling idea is a clear conceptual contribution, but its significance is currently limited by the absence of diagnostics confirming the theoretical precondition.

major comments (2)
  1. [Experiments] The central claim that the method 'yields a relatively noise-free annotation dataset' depends on the two active noise sampling strategies achieving the noise-identification accuracy threshold implied by the cited theoretical work. No ablation, precision/recall diagnostic, or explicit check against that threshold is reported, which is load-bearing for the noise-resilience and data-efficiency results.
  2. [Abstract] The abstract asserts experimental superiority under the same annotation budget but supplies no datasets, noise rates, baselines, or quantitative metrics, preventing evaluation of whether the claimed gains actually materialize or depend on the unverified precondition.
minor comments (1)
  1. [Abstract] The phrase 'a certain level of ability' in the abstract and introduction is too vague to connect the implementation to the theoretical precondition; a concrete accuracy target or reference to the cited work's requirement would clarify the link.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback. We address the major comments point by point below, acknowledging where the manuscript requires strengthening and outlining planned revisions.

read point-by-point responses

  1. Referee: [Experiments] The central claim that the method 'yields a relatively noise-free annotation dataset' depends on the two active noise sampling strategies achieving the noise-identification accuracy threshold implied by the cited theoretical work. No ablation, precision/recall diagnostic, or explicit check against that threshold is reported, which is load-bearing for the noise-resilience and data-efficiency results.

    Authors: We agree that the noise-identification accuracy precondition from the cited theory is central to the claims and that explicit verification is needed. The manuscript reports overall performance gains but does not include precision/recall diagnostics or direct checks against the threshold. In revision we will add these analyses, including ablations on the two sampling strategies and empirical confirmation that the precondition holds in the evaluated settings. revision: yes

  2. Referee: [Abstract] The abstract asserts experimental superiority under the same annotation budget but supplies no datasets, noise rates, baselines, or quantitative metrics, preventing evaluation of whether the claimed gains actually materialize or depend on the unverified precondition.

    Authors: The abstract was kept concise to provide a high-level overview. We acknowledge that including concrete details would improve evaluability. In the revised manuscript we will update the abstract to specify the datasets, noise rates, baselines, and key quantitative metrics. revision: yes

Circularity Check

0 steps flagged

No circularity; empirical method and results are independent of internal definitions or self-referential fits.

full rationale

The paper contains no derivations, equations, or fitted parameters that reduce claims to inputs by construction. It motivates the re-labeling approach by citing external prior theoretical work on noise identification thresholds, without defining any internal quantity in terms of the target result or renaming a fitted statistic as a prediction. The central claims are end-to-end experimental comparisons under fixed annotation budgets; these rest on observable performance metrics rather than any self-definitional or self-citation chain. No load-bearing step collapses to a tautology.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

Abstract-only review; no equations, parameters, or explicit assumptions beyond the cited theoretical work are provided.

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discussion (0)

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