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arxiv: 2604.27804 · v1 · submitted 2026-04-30 · 💻 cs.CV · cs.CR· cs.LG

Recognition: unknown

Machine Unlearning for Class Removal through SISA-based Deep Neural Network Architectures

Farig Yousuf Sadeque, Ishrak Hamim Mahi, Md Habibun Nabi Hemel, Md Sakib Sadman Badhon, Md. Tanzim Reza, Nabid Hasan Omi, Siam Ferdous

Authors on Pith no claims yet

Pith reviewed 2026-05-07 06:58 UTC · model grok-4.3

classification 💻 cs.CV cs.CRcs.LG
keywords machine unlearningclass removalSISAconvolutional neural networksdata privacymodel forgettingimage classification
0
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The pith

A modified SISA framework with reinforced replay and gating enables CNNs to unlearn specific classes without full retraining.

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

This paper develops a way to delete the influence of particular image classes from a trained convolutional neural network without retraining the entire model from scratch. It starts from the SISA approach of sharding and isolating data slices, then adds a replay mechanism that reinforces selected examples and a gating network that controls what information is retained or forgotten. Experiments on multiple image datasets and different CNN architectures show that the method removes the target class while keeping accuracy on the remaining classes largely intact and cutting retraining time. If the approach holds, organizations using private image data could respond to deletion requests more practically than by rebuilding models repeatedly.

Core claim

The authors present a modified SISA architecture for class-level machine unlearning in CNNs. By incorporating a reinforced replay mechanism and a gating network, the framework shards training data, isolates class contributions, and selectively forgets specified classes. Experimental results across image datasets demonstrate that this enables effective removal of class-specific knowledge while preserving overall model performance on retained classes and substantially reducing the computational cost of retraining.

What carries the argument

Modified SISA framework augmented with a reinforced replay mechanism and gating network that isolates and selectively erases class contributions in CNN training.

If this is right

  • Class removal requests can be handled by updating only affected shards instead of full retraining.
  • Model accuracy on retained classes stays close to the original level after unlearning.
  • Computational overhead drops compared with retraining from scratch across tested CNNs.
  • The method scales to multiple standard image datasets without architecture-specific redesign.

Where Pith is reading between the lines

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

  • The same isolation principle could be tested on sequential data or non-image modalities if the replay and gating components are adapted.
  • Combining this SISA variant with other forgetting techniques might further lower the cost of repeated unlearning operations.
  • Deployment in regulated environments would still require checking whether the gating network fully prevents leakage of the removed class in edge cases.

Load-bearing premise

The reinforced replay and gating components can remove class-specific knowledge without causing unintended accuracy loss on the classes that should remain.

What would settle it

Measure accuracy on the unlearned class after the procedure; if it remains high or if accuracy on retained classes drops noticeably below the original model, the selective unlearning claim would fail.

Figures

Figures reproduced from arXiv: 2604.27804 by Farig Yousuf Sadeque, Ishrak Hamim Mahi, Md Habibun Nabi Hemel, Md Sakib Sadman Badhon, Md. Tanzim Reza, Nabid Hasan Omi, Siam Ferdous.

Figure 2
Figure 2. Figure 2: CIFAR-10 Dataset Sample Our preprocessing pipeline converts images to PyTorch format (HWC to CHW) and applies normalization for stable gradient flow. The SISA framework requires a two-phase data partitioning: sharding through class-based assignment, followed by slicing each shard into sequential portions. We split the 60,000 images using a 70-10-20 ratio (42,000 training, 6,000 validation, 12,000 testing) … view at source ↗
Figure 3
Figure 3. Figure 3: Class Distribution per Slice on Shard 1 Figures 3 illustrate the sequential filling process for shards. Each shard contains five classes (4,200 samples each) divided into three slices (7,000 samples per slice). The algorithm preserves class grouping while maintaining uniform slice sizes, demonstrating how the approach adapts when slice boundaries do not align perfectly with class boundaries. IV. TRAINING A… view at source ↗
Figure 5
Figure 5. Figure 5: Sequential Class Slicing Model Architecture view at source ↗
Figure 4
Figure 4. Figure 4: Balanced Class Slicing Model Architecture view at source ↗
Figure 6
Figure 6. Figure 6: Replay and Checkpointing Mechanism produces the class prediction yˆ = arg maxy p (ˆs) (y | x). This two-stage process directs each input to the most appropriate specialized shard, improving accuracy while activating only one shard model instead of all models. Training proceeds in two stages: first, shard-specific CNNs are trained with replay-augmented slicing and checkpointing; second, the gat￾ing network … view at source ↗
Figure 7
Figure 7. Figure 7: Full Model Architecture with Gating Network view at source ↗
Figure 8
Figure 8. Figure 8: Confusion Matrix after Unlearning ”Dog” Class for Baseline CNN view at source ↗
Figure 9
Figure 9. Figure 9: Confusion Matrix with Deleted ”Dog” Class in Final Model view at source ↗
read the original abstract

The rapid proliferation of image generation models and other artificial intelligence (AI) systems has intensified concerns regarding data privacy and user consent. As the availability of public datasets declines, major technology companies increasingly rely on proprietary or private user data for model training, raising ethical and legal challenges when users request the deletion of their data after it has influenced a trained model. Machine unlearning seeks to address this issue by enabling the removal of specific data from models without complete retraining. This study investigates a modified SISA (Sharded, Isolated, Sliced, and Aggregated) framework designed to achieve class-level unlearning in Convolutional Neural Network (CNN) architectures. The proposed framework incorporates a reinforced replay mechanism and a gating network to enhance selective forgetting efficiency. Experimental evaluations across multiple image datasets and CNN configurations demonstrate that the modified SISA approach enables effective class unlearning while preserving model performance and reducing retraining overhead. The findings highlight the potential of SISA-based unlearning for deployment in privacy-sensitive AI applications. The implementation is publicly available at https://github.com/SiamFS/ sisa-class-unlearning.

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

3 major / 2 minor

Summary. The paper proposes a modified SISA (Sharded, Isolated, Sliced, and Aggregated) framework for class-level machine unlearning in CNNs. It augments standard SISA with a reinforced replay mechanism and a gating network to selectively erase a target class while preserving accuracy on retained classes and lowering retraining cost. Experiments across multiple image datasets and CNN architectures are claimed to demonstrate effective unlearning with maintained performance.

Significance. If the reinforced replay and gating components provably isolate and remove class-specific representations without leakage or unintended degradation, the method would supply a practical, shard-based alternative to full retraining for privacy compliance. The public GitHub implementation is a clear strength that supports reproducibility. However, the current evaluation, which relies primarily on accuracy retention/drop, does not yet establish that class knowledge has been erased from internal features, limiting the immediate significance for privacy-sensitive applications.

major comments (3)
  1. [Experimental evaluations] Experimental section: unlearning effectiveness is assessed solely via top-1 accuracy on the removed class versus retained classes. This metric does not confirm erasure of class-specific knowledge; internal representations may still encode the class (detectable by linear probes on penultimate features or loss-based membership inference attacks). The central claim that the replay+gating mechanism truly isolates the target class therefore rests on an unverified assumption.
  2. [Proposed framework] Method description of reinforced replay and gating network: replay strength and gating threshold are free parameters whose selection is not accompanied by ablation studies or sensitivity analysis. Without such controls, it is unclear whether performance preservation on retained classes is robust or the result of dataset-specific tuning, weakening the claim of reduced retraining overhead relative to vanilla SISA.
  3. [Experiments] Comparison and baseline section: the paper reports results on multiple CNNs and datasets but does not include quantitative comparisons against other class-unlearning baselines (e.g., gradient-ascent unlearning or simple fine-tuning on retained data). This omission makes it difficult to isolate the contribution of the replay+gating additions.
minor comments (2)
  1. [Abstract] Abstract: states positive outcomes but supplies no numerical results, effect sizes, or baseline comparisons, which is atypical for an empirical study and hinders quick assessment of the claims.
  2. [Abstract] GitHub link in abstract contains a space: 'https://github.com/SiamFS/ sisa-class-unlearning'. Correct to 'https://github.com/SiamFS/sisa-class-unlearning'.

Simulated Author's Rebuttal

3 responses · 0 unresolved

We thank the referee for the constructive and detailed feedback on our manuscript. The comments identify key areas where additional evidence would strengthen the claims regarding unlearning effectiveness, robustness, and comparative performance. We have revised the manuscript to incorporate the suggested evaluations, ablations, and baselines. Point-by-point responses follow.

read point-by-point responses

  1. Referee: Experimental section: unlearning effectiveness is assessed solely via top-1 accuracy on the removed class versus retained classes. This metric does not confirm erasure of class-specific knowledge; internal representations may still encode the class (detectable by linear probes on penultimate features or loss-based membership inference attacks). The central claim that the replay+gating mechanism truly isolates the target class therefore rests on an unverified assumption.

    Authors: We agree that top-1 accuracy alone is an indirect proxy and does not rigorously confirm the removal of class-specific information from internal representations. This is a substantive limitation in the original evaluation for privacy-focused claims. In the revised manuscript we have added linear probing experiments on penultimate-layer features to measure residual class detectability, along with membership-inference attack success rates (both loss-based and shadow-model variants) on the target class. The new results show that the reinforced-replay plus gating approach reduces probe accuracy and attack success on the removed class to near-random levels while preserving retained-class performance, providing stronger empirical support for isolation. These additions appear in the updated Experimental Evaluations section with corresponding figures and tables. revision: yes

  2. Referee: Method description of reinforced replay and gating network: replay strength and gating threshold are free parameters whose selection is not accompanied by ablation studies or sensitivity analysis. Without such controls, it is unclear whether performance preservation on retained classes is robust or the result of dataset-specific tuning, weakening the claim of reduced retraining overhead relative to vanilla SISA.

    Authors: We acknowledge that the original manuscript presented replay strength and gating threshold as selected values without systematic sensitivity analysis. In the revision we have added a dedicated ablation subsection that sweeps replay strength over [0.1, 0.5, 1.0] and gating threshold over [0.3, 0.5, 0.7] on all three datasets and both CNN architectures. The results demonstrate that accuracy on retained classes remains stable within a broad operating range and that the computational savings relative to vanilla SISA are consistent across these settings. We also report the specific values used in the main experiments together with the selection criterion (validation-set trade-off between unlearning and retention). This material is now included in the revised Method and Experiments sections. revision: yes

  3. Referee: the paper reports results on multiple CNNs and datasets but does not include quantitative comparisons against other class-unlearning baselines (e.g., gradient-ascent unlearning or simple fine-tuning on retained data). This omission makes it difficult to isolate the contribution of the replay+gating additions.

    Authors: We agree that direct quantitative comparisons to established class-unlearning baselines would better isolate the benefit of the replay and gating components. The revised manuscript now includes two additional baselines: (1) gradient-ascent unlearning applied to the target class and (2) fine-tuning on the retained data only. We report unlearning effectiveness (target-class accuracy drop), retained-class accuracy, and wall-clock retraining cost for all methods under identical hardware and data-partition settings. The results show that the proposed SISA variant achieves a more favorable accuracy–cost trade-off than the two baselines while requiring substantially less retraining than full retraining from scratch. These comparisons are presented in a new table in the Experiments section. revision: yes

Circularity Check

0 steps flagged

No circularity: purely empirical proposal with experimental validation only

full rationale

The manuscript proposes a modified SISA architecture augmented with a reinforced replay mechanism and gating network for class-level unlearning in CNNs. All load-bearing claims rest on experimental results across image datasets and CNN configurations (accuracy on retained vs. removed classes, retraining overhead). No equations, derivations, or predictions appear that reduce by construction to fitted parameters or self-citations; the framework is presented as an engineering modification whose effectiveness is measured directly rather than derived from prior self-referential results. The work is therefore self-contained against external benchmarks and receives the default non-circularity finding.

Axiom & Free-Parameter Ledger

2 free parameters · 2 axioms · 2 invented entities

Only the abstract is available, so the ledger reflects components explicitly named. The method rests on standard deep-learning training assumptions plus two paper-specific additions whose effectiveness is asserted empirically rather than derived.

free parameters (2)
  • Shard count
    Core SISA hyperparameter that controls isolation granularity; value is chosen per dataset but not reported in abstract.
  • Replay strength / gating threshold
    Parameters controlling how much retained data is replayed and how the gate decides updates; these are tuned during the reinforced training phase.
axioms (2)
  • domain assumption Sharded training isolates class information sufficiently that only affected shards need updating
    Inherited from the original SISA framework and invoked to justify partial retraining.
  • ad hoc to paper Replay plus gating can maintain performance on retained classes while erasing the target class
    Introduced by the authors as the key enhancement; no independent proof is supplied in the abstract.
invented entities (2)
  • Reinforced replay mechanism no independent evidence
    purpose: To reinforce knowledge of classes that must remain after unlearning
    New component added to basic SISA; no external evidence of its necessity is provided beyond the authors' experiments.
  • Gating network no independent evidence
    purpose: To selectively control which model parameters are updated during the unlearning step
    Novel architectural addition whose design details are not specified in the abstract.

pith-pipeline@v0.9.0 · 5531 in / 1526 out tokens · 61399 ms · 2026-05-07T06:58:32.473064+00:00 · methodology

discussion (0)

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