arXiv:2504.15827v2 Announce Type: replace-cross
Abstract: Existing machine unlearning (MU) approaches exhibit significant sensitivity to hyperparameters, requiring meticulous tuning that limits practical deployment. In this work, we first empirically demonstrate the instability and suboptimal performance of existing popular MU methods when deployed in different scenarios. To address this issue, we propose Dual Optimizer (DualOptim), which incorporates adaptive learning rate and decoupled momentum factors. Empirical and theoretical evidence demonstrates that DualOptim contributes to effective and stable unlearning. Through extensive experiments, we show that DualOptim can significantly boost MU efficacy and stability across diverse tasks, including image classification, image generation, and large language models, making it a versatile approach to empower existing MU algorithms.
Generative Semantic Coding for Ultra-Low Bitrate Visual Communication and Analysis
arXiv:2510.27324v1 Announce Type: cross Abstract: We consider the problem of ultra-low bit rate visual communication for remote vision analysis, human interactions and control in challenging