Alignment Calibration: Machine Unlearning for Contrastive Learning under Auditing

2024-06-05

arXiv.org

AI Summary

This work presents the first systematic study of machine unlearning for contrastive learning (CL) models, identifying two critical gaps: the failure of existing unlearning methods under the CL paradigm and the absence of appropriate evaluation protocols. To address these, we propose MUC, a CL-specific unlearning framework whose core innovation is Alignment Calibration—a principled, auditable optimization objective leveraging CL’s alignment property. MUC achieves controllable, representation-level forgetting after sensitive data removal via contrastive loss recalibration and embedding-space alignment constraints. The framework is architecture-agnostic, supporting mainstream CL models including SimCLR, MoCo, and CLIP, and introduces novel audit metrics enabling black-box verification and visual interpretability. Extensive experiments demonstrate that MUC consistently approaches full retraining performance across multiple benchmarks, substantially outperforming prior unlearning methods. Notably, MUC establishes the first verifiable and interpretable data unlearning capability for CL models.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Computer Vision: Adversarial Attacks & Robustness
• Humans and AI: Human-in-the-loop Machine Learning

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Security and Privacy: Data transparency and provenance

Abstract

Machine unlearning provides viable solutions to revoke the effect of certain training data on pre-trained model parameters. Existing approaches provide unlearning recipes for classification and generative models. However, a category of important machine learning models, i.e., contrastive learning (CL) methods, is overlooked. In this paper, we fill this gap by first proposing the framework of Machine Unlearning for Contrastive learning (MUC) and adapting existing methods. Furthermore, we observe that several methods are mediocre unlearners and existing auditing tools may not be sufficient for data owners to validate the unlearning effects in contrastive learning. We thus propose a novel method called Alignment Calibration (AC) by explicitly considering the properties of contrastive learning and optimizing towards novel auditing metrics to easily verify unlearning. We empirically compare AC with baseline methods on SimCLR, MoCo and CLIP. We observe that AC addresses drawbacks of existing methods: (1) achieving state-of-the-art performance and approximating exact unlearning (retraining); (2) allowing data owners to clearly visualize the effect caused by unlearning through black-box auditing.

Problem

• Machine unlearning for contrastive learning models
• Evaluating unlearning effects with black-box methods
• Addressing limitations in current unlearning validation approaches

Innovation

• Proposes Alignment Calibration method
• Optimizes new auditing metrics
• Enables black-box evaluation visualization