CERSA derives low-rank fine-tuning subspaces from SVD principal components that retain 90-95% spectral energy, delivering higher performance than LoRA and other PEFT baselines at substantially lower memory cost across vision, generation, and language tasks.
Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies , pages=
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CERSA: Cumulative Energy-Retaining Subspace Adaptation for Memory-Efficient Fine-Tuning
CERSA derives low-rank fine-tuning subspaces from SVD principal components that retain 90-95% spectral energy, delivering higher performance than LoRA and other PEFT baselines at substantially lower memory cost across vision, generation, and language tasks.