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Decoupling the Class Label and the Target Concept in Machine Unlearning

ICLR 2026 Conference SubmissionAnonymous Authors
OpenReview Score: 7.0Download Report PDF
Machine UnlearningLabel Domain Mismatch
Abstract:

Machine unlearning as an emerging research topic for data regulations, aims to adjust a trained model to approximate a retrained one that excludes a portion of training data. Previous studies showed that class-wise unlearning is effective in forgetting the knowledge of a training class, either through gradient ascent on the forgetting data or fine-tuning with the remaining data. However, while these methods are useful, they are insufficient as the class label and the target concept are often considered to coincide. In this work, we expand the scope by considering the label domain mismatch and investigate three problems beyond the conventional all matched forgetting, e.g., target mismatch, model mismatch, and data mismatch forgetting. We systematically analyze the new challenges in restrictively forgetting the target concept and also reveal crucial forgetting dynamics in the representation level to realize these tasks. Based on that, we propose a general framework, namely, TARget-aware Forgetting (TARF). It enables the additional tasks to actively forget the target concept while maintaining the rest part, by simultaneously conducting annealed gradient ascent on the forgetting data and selected gradient descent on the hard-to-affect remaining data. Various experiments under our new settings are conducted to demonstrate the effectiveness of our TARF.

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This report is AI-GENERATED using Large Language Models and WisPaper (A scholar search engine). It analyzes academic papers' tasks and contributions against retrieved prior work. While this system identifies POTENTIAL overlaps and novel directions, ITS COVERAGE IS NOT EXHAUSTIVE AND JUDGMENTS ARE APPROXIMATE. These results are intended to assist human reviewers and SHOULD NOT be relied upon as a definitive verdict on novelty.
NOTE that some papers exist in multiple, slightly different versions (e.g., with different titles or URLs). The system may retrieve several versions of the same underlying work. The current automated pipeline does not reliably align or distinguish these cases, so human reviewers will need to disambiguate them manually.
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Overview

Overall Novelty Assessment

The paper proposes TARF, a framework for machine unlearning that decouples class labels from target concepts, addressing scenarios where the two do not align. It sits in the 'Label-Concept Mismatch Unlearning' leaf of the taxonomy, which contains only two papers including this one. This is a notably sparse research direction compared to more crowded branches like 'Concept-Level Unlearning Methods' or 'Class-Level Unlearning Methods', suggesting the paper explores a relatively underexplored problem space within the broader unlearning literature.

The taxonomy reveals that most prior work assumes label-concept alignment, with neighboring branches focusing on either concept-level removal (e.g., disentangling biased knowledge, causal unlearning) or class-level forgetting (e.g., gradient-based weight manipulation, distillation methods). The paper's position bridges these areas by explicitly addressing mismatch scenarios—target mismatch, model mismatch, and data mismatch—that fall outside the scope of traditional concept-level or class-level methods. Its sibling paper in the same leaf examines military helicopter unlearning, indicating shared interest in label-concept divergence but different application contexts.

Among the 22 candidates examined through limited semantic search, none were found to clearly refute any of the three main contributions. The first contribution (decoupling labels and concepts) examined 2 candidates with no refutations; the second (representation-level forgetting dynamics) and third (TARF framework) each examined 10 candidates with no refutations. This suggests that within the search scope, the specific combination of addressing label-concept mismatch through representation-level analysis and annealed gradient ascent appears relatively novel, though the limited search scale means potentially relevant work outside the top-22 semantic matches may exist.

Based on the available signals from 22 examined candidates and the sparse taxonomy leaf, the work appears to occupy a distinct position in the unlearning landscape. The explicit focus on label-concept decoupling and the systematic treatment of three mismatch scenarios differentiate it from neighboring concept-level and class-level methods. However, the limited search scope and small sibling set mean this assessment reflects novelty within the examined literature rather than an exhaustive field-wide comparison.

Taxonomy

Core-task Taxonomy Papers
27
3
Claimed Contributions
22
Contribution Candidate Papers Compared
0
Refutable Paper

Research Landscape Overview

Core task: Decoupling class labels and target concepts in machine unlearning. The field of machine unlearning has evolved to address the challenge that class labels and the underlying concepts a model learns do not always align perfectly. The taxonomy reflects this complexity through several main branches: Concept-Level Unlearning Methods focus on removing specific learned features or attributes rather than entire classes, while Class-Level Unlearning Methods target traditional label-based removal. Label-Concept Mismatch Unlearning explicitly tackles scenarios where the two diverge, such as when a class contains multiple distinct concepts or when spurious correlations exist. Domain and Modality-Specific Unlearning adapts techniques to particular data types like images or graphs, and Specialized Unlearning Contexts address settings such as federated learning. Unlearning Robustness and Security examines adversarial challenges, while Unlearning Surveys and Frameworks provide overarching perspectives. Works like SalUn Gradient Saliency[4] and Score Forgetting Distillation[5] illustrate gradient-based and distillation-based approaches, respectively, while Federated Unlearning[8] and Hyperbolic Multimodal Unlearning[9] show domain-specific adaptations. A particularly active line of work explores how to disentangle biased or spurious knowledge from legitimate class information, as seen in Disentangling Biased Knowledge[1] and CaMU Causal Unlearning[2], which leverage causal reasoning to isolate unwanted associations. Another thread addresses noisy or imbalanced data scenarios, exemplified by Longtailed Label Noise[3], where label quality itself complicates the unlearning target. The original paper, Decoupling Class Label[0], sits squarely within the Label-Concept Mismatch Unlearning branch alongside Military Helicopter Unlearning[18], emphasizing the need to separate what a class name denotes from what the model has actually encoded. Compared to works like Disentangling Biased Knowledge[1], which focus on bias removal, Decoupling Class Label[0] more directly interrogates the structural misalignment between labels and learned representations, offering a complementary perspective on ensuring precise and interpretable unlearning.

Claimed Contributions

Decoupling class label and target concept in machine unlearning

The authors introduce new unlearning settings that decouple the class label from the target concept, modeling scenarios where the forgetting data, model output, and target concept have mismatched label domains. This expands beyond the conventional assumption that the target concept coincides with the class label.

2 retrieved papers
Systematic analysis of forgetting dynamics at the representation level

The authors provide a systematic empirical and theoretical analysis of how representation-level dynamics affect unlearning under label domain mismatch. They identify challenges such as insufficient representation and decomposition lacking, and derive formal results connecting representation similarity to forgetting dynamics.

10 retrieved papers
TARF framework for target-aware forgetting

The authors propose TARF, a unified framework that addresses mismatched unlearning scenarios through annealed forgetting and target-aware retaining. The method dynamically identifies target data and separates entangled representations to approximate retraining on the retaining data.

10 retrieved papers

Core Task Comparisons

Comparisons with papers in the same taxonomy category

Contribution Analysis

Detailed comparisons for each claimed contribution

Contribution

Decoupling class label and target concept in machine unlearning

The authors introduce new unlearning settings that decouple the class label from the target concept, modeling scenarios where the forgetting data, model output, and target concept have mismatched label domains. This expands beyond the conventional assumption that the target concept coincides with the class label.

Contribution

Systematic analysis of forgetting dynamics at the representation level

The authors provide a systematic empirical and theoretical analysis of how representation-level dynamics affect unlearning under label domain mismatch. They identify challenges such as insufficient representation and decomposition lacking, and derive formal results connecting representation similarity to forgetting dynamics.

Contribution

TARF framework for target-aware forgetting

The authors propose TARF, a unified framework that addresses mismatched unlearning scenarios through annealed forgetting and target-aware retaining. The method dynamically identifies target data and separates entangled representations to approximate retraining on the retaining data.

Table of Contents

Decoupling the Class Label and the Target Concept in Machine Unlearning | Novelty Validation