2 papers
cs.LG2025
SEMU: Singular Value Decomposition for Efficient Machine Unlearning
Marcin Sendera, Åukasz Struski, Kamil KsiÄ Å¼ek +3
While the capabilities of generative foundational models have advanced rapidly in recent years, methods to prevent harmful and unsafe behaviors remain underdeveloped. Among the pre…
cs.CV2024
AutoLoRA: AutoGuidance Meets Low-Rank Adaptation for Diffusion Models
Artur Kasymov, Marcin Sendera, MichaÅ StypuÅkowski +2
Low-rank adaptation (LoRA) is a fine-tuning technique that can be applied to conditional generative diffusion models. LoRA utilizes a small number of context examples to adapt the…