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Found 8 papers, 8 papers with code
Challenging Forgets: Unveiling the Worst-Case Forget Sets in Machine Unlearning
This leads to the problem of machine unlearning (MU), aiming to eliminate the influence of chosen data points on model performance, while still maintaining the model's utility post-unlearning.
UnlearnCanvas: A Stylized Image Dataset to Benchmark Machine Unlearning for Diffusion Models
The rapid advancement of diffusion models (DMs) has not only transformed various real-world industries but has also introduced negative societal concerns, including the generation of harmful content, copyright disputes, and the rise of stereotypes and biases.
SalUn: Empowering Machine Unlearning via Gradient-based Weight Saliency in Both Image Classification and Generation
To address these challenges, we introduce the concept of 'weight saliency' for MU, drawing parallels with input saliency in model explanation.
To Generate or Not? Safety-Driven Unlearned Diffusion Models Are Still Easy To Generate Unsafe Images ... For Now
Our results demonstrate the effectiveness and efficiency merits of UnlearnDiffAtk over the state-of-the-art adversarial prompt generation method and reveal the lack of robustness of current safety-driven unlearning techniques when applied to DMs.
DeepZero: Scaling up Zeroth-Order Optimization for Deep Model Training
Our extensive experiments show that DeepZero achieves state-of-the-art (SOTA) accuracy on ResNet-20 trained on CIFAR-10, approaching FO training performance for the first time.
Model Sparsity Can Simplify Machine Unlearning
We show in both theory and practice that model sparsity can boost the multi-criteria unlearning performance of an approximate unlearner, closing the approximation gap, while continuing to be efficient.
Can Adversarial Examples Be Parsed to Reveal Victim Model Information?
We call this 'model parsing of adversarial attacks' - a task to uncover 'arcana' in terms of the concealed VM information in attacks.
Complex Locomotion Skill Learning via Differentiable Physics
Differentiable physics enables efficient gradient-based optimizations of neural network (NN) controllers.
