arXiv Machine Learning
Not All Forgetting Is Equal: Retention Dynamics in Fine-Tuned Image Classifiers
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arXiv Machine Learning
1d ago
The Null Is the Hard Part: Exact Tests for Memorization in Generative Models
The paper critiques current memorization audits for generative models, arguing that lacking a proper null distribution leads to misleading conclusions. It introduces two exact null tests—one permutation test for whole models and a calibrated test for single images—showing that many previously flagged memorizations disappear under these stricter controls. The authors also propose a scale‑restricted statistic based on the Intersection Euler Characteristic Profile to better detect distinct copied images.
arXiv AI
Jun 3
PURGE: Projected Unlearning via Retain-Guided Erasure
arXiv:2606. 03808v1 Announce Type: cross Abstract: We propose PURGE, a machine unlearning algorithm built on a simple but an under-exploited observation: continual learning (CL) and machine unlearning (MU) which are fundamentally dual problems.
arXiv AI
Jun 29
Do Vision Models Truly Forget? New Findings from Representation-Level Certification of Visual Unlearning in Vertical Federated Learning
arXiv:2605. 20282v3 Announce Type: replace-cross Abstract: Machine unlearning in Vertical Federated Learning (VFL) has attracted growing interest, yet existing methods certify forgetting solely using output-level metrics.
arXiv AI
Jun 2
Can Vision Models Truly Forget? Mirage: Representation-Level Certification of Visual Unlearning
arXiv:2605. 20282v2 Announce Type: replace-cross Abstract: Machine unlearning in Vertical Federated Learning (VFL) has attracted growing interest, yet existing methods certify forgetting solely using output-level metrics.
arXiv Machine Learning
Jul 15
Forgetful Attention: A Trainable Support-Vector Memory with Certified Selection and Exact Unlearning
arXiv:2607. 12204v1 Announce Type: new Abstract: Attention can be viewed as an online learner over context, yet existing test-time memories cannot certify that dropping a token leaves outputs unchanged or delete its influence outright.
arXiv Machine Learning
Aug 3
TOOD: Task-Aware Out-of-Distribution Score Calibration for Continual Learners
arXiv:2607. 29592v1 Announce Type: cross Abstract: The primary challenge of continual learning (CL) systems is to learn new tasks while remaining performant on previously learned tasks.