Title: A Framework for Sample and Objective Forgetting: Pull-to-Outlier & Contrastive Objective-level (POCO) Unlearning
Primary area: unsupervised, self-supervised, semi-supervised, and supervised representation learning
Keywords: Machine Unlearning, Unsupervised Forgetting, Instance-Level Forgetting, Objective-Level Forgetting

Abstract: Current Machine Unlearning (MU) methods require full retraining or extensive fine-tuning, lack formal removal criteria, and focus only on sample-level forgetting, limiting their practicality. We address these gaps with two lightweight, projection-only techniques operating above frozen feature extractors. Pull-to-Outlier Unlearning (POU) offers a transparent, unsupervised geometric removal method by displacing embeddings of unwanted samples or entire classes into synthetic outlier regions, while preserving downstream performance and distilling knowledge of the remaining data. To the best of our knowledge, Contrastive Objective-level Unlearning (COU) is the first method to remove learned objectives. It perturbs projection weights to eliminate a target task’s influence. Then it realigns the original data manifold, which can provide the possibility for managing agentic learning behaviours. We validate POU on CIFAR10, CIFAR100, and Caltech-256 with ResNet-based backbones, showing efficient instance and class forgetting with minimal impact on retained accuracy. COU is tested on DINO and CLIP feature representations, demonstrating effective objective-level erasure while preserving all non-target tasks.