3 papers
cs.CV2024
Mask and Compress: Efficient Skeleton-based Action Recognition in Continual Learning
Matteo Mosconi, Andriy Sorokin, Aniello Panariello +6
The use of skeletal data allows deep learning models to perform action recognition efficiently and effectively. Herein, we believe that exploring this problem within the context of…
cs.CV2024
DUCK: Distance-based Unlearning via Centroid Kinematics
Marco Cotogni, Jacopo Bonato, Luigi Sabetta +2
Machine Unlearning is rising as a new field, driven by the pressing necessity of ensuring privacy in modern artificial intelligence models. This technique primarily aims to eradica…
cs.CV2024
Is Retain Set All You Need in Machine Unlearning? Restoring Performance of Unlearned Models with Out-Of-Distribution Images
Jacopo Bonato, Marco Cotogni, Luigi Sabetta
In this paper, we introduce Selective-distillation for Class and Architecture-agnostic unleaRning (SCAR), a novel approximate unlearning method. SCAR efficiently eliminates specifi…