Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation

• URL: http://arxiv.org/abs/2501.15377v1
• Date: Sun, 26 Jan 2025 03:22:22 GMT
• Title: Fine Tuning without Catastrophic Forgetting via Selective Low Rank Adaptation
• Authors: Reza Akbarian Bafghi, Carden Bagwell, Avinash Ravichandran, Ashish Shrivastava, Maziar Raissi,
• Abstract summary: Fine-tuning can reduce robustness to distribution shifts, impacting out-of-distribution (OOD) performance. We propose a parameter-efficient fine-tuning (PEFT) method, using an indicator function to selectively activate Low-Rank Adaptation (LoRA) blocks. We demonstrate that effective fine-tuning can be achieved with as few as 5% of active blocks, substantially improving efficiency.
• Score: 13.084333776247743
• License:
• Abstract: Adapting deep learning models to new domains often requires computationally intensive retraining and risks catastrophic forgetting. While fine-tuning enables domain-specific adaptation, it can reduce robustness to distribution shifts, impacting out-of-distribution (OOD) performance. Pre-trained zero-shot models like CLIP offer strong generalization but may suffer degraded robustness after fine-tuning. Building on Task Adaptive Parameter Sharing (TAPS), we propose a simple yet effective extension as a parameter-efficient fine-tuning (PEFT) method, using an indicator function to selectively activate Low-Rank Adaptation (LoRA) blocks. Our approach minimizes knowledge loss, retains its generalization strengths under domain shifts, and significantly reduces computational costs compared to traditional fine-tuning. We demonstrate that effective fine-tuning can be achieved with as few as 5\% of active blocks, substantially improving efficiency. Evaluations on pre-trained models such as CLIP and DINO-ViT demonstrate our method's broad applicability and effectiveness in maintaining performance and knowledge retention.

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