Flow matching-based generative models for MIMO channel estimation

The paper titled 'Parameter-Efficient MoE LoRA for Few-Shot Multi-Style Editing' addresses the challenges faced by general image editing models when adapting to new styles. It proposes a novel few-shot style editing framework and introduces a benchmark dataset comprising five distinct styles. The framework utilizes a parameter-efficient multi-style Mixture-of-Experts Low-Rank Adaptation (MoE LoRA) that employs style-specific and style-shared routing mechanisms to fine-tune multiple styles effectively. This approach aims to enhance the performance of image editing models with minimal data.