AI RESEARCH
FAAR: Efficient Frequency-Aware Multi-Task Fine-Tuning via Automatic Rank Selection
arXiv CS.CV
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ArXi:2603.20403v1 Announce Type: new Adapting models pre-trained on large-scale datasets is a proven way to reach strong performance quickly for down-stream tasks. However, the growth of state-of-the-art mod-els makes traditional full fine-tuning unsuitable and difficult, especially for multi-task learning (MTL) where cost scales with the number of tasks. As a result, recent studies investigate parameter-efficient fine-tuning (PEFT) using low-rank adaptation to significantly reduce the number of trainable parameters.