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Found 8 papers, 2 papers with code
Unified Gradient-Based Machine Unlearning with Remain Geometry Enhancement
Approximate MU is a practical method for large-scale models.
Pursuing Feature Separation based on Neural Collapse for Out-of-Distribution Detection
In the open world, detecting out-of-distribution (OOD) data, whose labels are disjoint with those of in-distribution (ID) samples, is important for reliable deep neural networks (DNNs).
Towards Natural Machine Unlearning
Towards more \textit{natural} machine unlearning, we inject correct information from the remaining data to the forgetting samples when changing their labels.
Friendly Sharpness-Aware Minimization
By decomposing the adversarial perturbation in SAM into full gradient and stochastic gradient noise components, we discover that relying solely on the full gradient component degrades generalization while excluding it leads to improved performance.
Remaining-data-free Machine Unlearning by Suppressing Sample Contribution
We first theoretically discover that sample's contribution during the process will reflect in the learned model's sensitivity to it.
Low-Dimensional Gradient Helps Out-of-Distribution Detection
To bridge this gap, in this paper, we conduct a comprehensive investigation into leveraging the entirety of gradient information for OOD detection.
Self-Ensemble Protection: Training Checkpoints Are Good Data Protectors
In this paper, we uncover them by model checkpoints' gradients, forming the proposed self-ensemble protection (SEP), which is very effective because (1) learning on examples ignored during normal training tends to yield DNNs ignoring normal examples; (2) checkpoints' cross-model gradients are close to orthogonal, meaning that they are as diverse as DNNs with different architectures.
FG-UAP: Feature-Gathering Universal Adversarial Perturbation
Deep Neural Networks (DNNs) are susceptible to elaborately designed perturbations, whether such perturbations are dependent or independent of images.
