arXiv AI
Jul 1
Not Every Time and Frequency Need to Be Forgotten in Diffusion Unlearning
arXiv:2510. 17917v2 Announce Type: replace-cross Abstract: Data unlearning aims to remove the influence of specific training samples from a trained model.
Mitigating Sequential Reappearance in Diffusion Data-Point Unlearning
The paper introduces a new evaluation protocol for diffusion data‑point unlearning that tracks whether each target is forgotten, remains forgotten, or reappears during subsequent deletions. It identifies a failure mode called sequential reappearance, where an instance that was initially forgotten later returns to the memorized regime without re‑use of the deleted data or adversarial fine‑tuning. The study also finds that reappearing targets exhibit a sharper local denoising‑loss geometry after deletion than those that remain forgotten.
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arXiv AI
Jul 3
Locality-Aware Continual Unlearning for Diffusion Models
arXiv:2512. 02657v2 Announce Type: replace-cross Abstract: Real-world deployment of text-to-image diffusion models requires continual concept removal as new privacy, copyright, or safety obligations arise over time.
arXiv AI
Sep 1
On the Plasticity Collapse in Continual Machine Unlearning
The paper investigates continual machine unlearning, where models must forget data over time. It identifies a fundamental issue called plasticity collapse, where successive unlearning requests cause geometric constraints that saturate parameter space, leading to two failure modes: forward failure (reduced forgetting quality) and backward failure (re‑memorization). Experiments across architectures and datasets confirm that plasticity collapse is a pervasive problem in continual unlearning.
arXiv AI
Jun 2
PECKER: A Precisely Efficient Critical Knowledge Erasure Recipe For Machine Unlearning in Diffusion Models
arXiv:2604. 05634v2 Announce Type: replace Abstract: Machine unlearning (MU) has become a critical technique for GenAI models' safe and compliant operation.
arXiv Computer Vision
4d ago
Weeding Out Bad Seeds: Initial-Noise-Robust Unlearning for Text-to-Image Diffusion Models
arXiv:2609.37537v1 Announce Type: new Abstract: Machine unlearning has emerged as a critical post-hoc safety measure to erase sensitive concepts from Text-to-Image (T2I) models without prohibitive re...
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
Aug 5
Trajectory-Guided Forget-Recover Network for Continual LLM Unlearning
arXiv:2608. 03123v1 Announce Type: cross Abstract: Machine unlearning aims to eliminate the influence of sensitive data on a model.
Hugging Face Trending Papers
Aug 4
Trajectory-Guided Forget-Recover Network for Continual LLM Unlearning
Machine unlearning aims to eliminate the influence of sensitive data on a model. In the real world, unlearning requests arrive continually, which gives rise to two challenges.
arXiv AI
Sep 10
CUNO: Curriculum and Preference Optimization for Stable Graph Unlearning under Mass Deletion
CUNO is a curriculum‑based graph unlearning framework that progressively removes a designated set of training samples, ordering them by estimated unlearning difficulty across multiple stages. It also introduces a distribution‑level negative preference optimization objective at each stage to steer the model away from its original behavior on the current forget subset while preserving performance on retained data. Experiments show that CUNO mitigates catastrophic unlearning, retaining 74% of original utility at 20% deletion and more than half at 50% deletion, outperforming existing methods.
arXiv AI
Aug 5
One-Point Contraction: Erasing Representational Separability toward Irreversible Deep Forgetting
arXiv:2507. 07754v3 Announce Type: replace-cross Abstract: Machine unlearning is usually evaluated by what the classifier outputs: forget-set accuracy, confidence, membership-inference scores.
arXiv Machine Learning
Aug 3
When Unlearning Fails: Reliable Data Deletion under Post-Training in Agent Networks
arXiv:2607. 28829v1 Announce Type: cross Abstract: Self-improving federated agent networks keep training after deployment by collecting new trajectories with the current policy and feeding them back into later rounds.
arXiv Machine Learning
Jun 10
The Emergence of Reproducibility and Generalizability in Diffusion Models
arXiv:2310. 05264v5 Announce Type: replace Abstract: In this work, we investigate an intriguing and prevalent phenomenon of diffusion models which we term as "consistent model reproducibility": given the same starting noise input and a deterministic sampler, different diffusion models often yield remarkably similar outputs.