AI summaryⓘ
The authors study how machine learning models 'unlearn' certain information, like forgetting specific data or classes. They find that many popular methods seem to forget this information only because the last part of the model is changed, but the underlying features still remember it and can bring back the forgotten info easily. They show that focusing on aligning the model's internal features with its classifier helps true forgetting happen without losing performance on what should be remembered. To improve this, the authors propose a new method that explicitly aligns features and classifiers, tested on benchmarks with good results. Their work suggests researchers should check the model's inner workings when evaluating unlearning methods.
machine unlearningneural collapseclassifierfeature alignmentlinear probingrepresentation learningfine-tuningclass-mean featuresmodel forgettingretain accuracy
Authors
Yichen Gao, Altay Unal, Akshay Rangamani, Zhihui Zhu
Abstract
While numerous machine unlearning (MU) methods have recently been developed with promising results in erasing the influence of forgotten data, classes, or concepts, they are also highly vulnerable-for example, simple fine-tuning can inadvertently reintroduce erased concepts. In this paper, we address this contradiction by examining the internal representations of unlearned models, in contrast to prior work that focuses primarily on output-level behavior. Our analysis shows that many state-of-the-art MU methods appear successful mainly due to a misalignment between last-layer features and the classifier, a phenomenon we call feature-classifier misalignment. In fact, hidden features remain highly discriminative, and simple linear probing can recover near-original accuracy. Assuming neural collapse in the original model, we further demonstrate that adjusting only the classifier can achieve negligible forget accuracy while preserving retain accuracy, and we corroborate this with experiments using classifier-only fine-tuning. Motivated by these findings, we propose MU methods based on a class-mean features (CMF) classifier, which explicitly enforces alignment between features and classifiers. Experiments on standard benchmarks show that CMF-based unlearning reduces forgotten information in representations while maintaining high retain accuracy, highlighting the need for faithful representation-level evaluation of MU.