Advancements in Efficient Neural Network Fine-Tuning

The field of neural network fine-tuning is witnessing significant advancements, with a focus on improving efficiency and adaptability. Researchers are exploring novel methods to reduce computational costs and storage requirements, while maintaining or enhancing performance. One notable direction is the development of parameter-efficient fine-tuning techniques, such as Low-Rank Adaptation (LoRA) and its variants, which enable efficient model adaptation while reducing computational overhead. Another area of interest is the development of methods that can adapt to new tasks and domains without requiring extensive retraining or fine-tuning. These advancements have the potential to make neural networks more accessible and applicable to a wide range of tasks and domains. Noteworthy papers include: Gradient-Informed Fine-Tuning (GIFT) which achieves up to 28% relative accuracy improvement compared to the baseline performance under noise misspecification. WaRA, a novel PEFT method that leverages wavelet transforms to decompose the weight update matrix into a multi-resolution representation, performs superior on diverse vision tasks. GORP, a novel training strategy that synergistically combines full and low-rank parameters, overcomes the limitations of LoRA and achieves superior performance on continual learning benchmarks.

Sources

In situ fine-tuning of in silico trained Optical Neural Networks

Are Fast Methods Stable in Adversarially Robust Transfer Learning?

A Scalable Approach for Safe and Robust Learning via Lipschitz-Constrained Networks

WaRA: Wavelet Low Rank Adaptation

Gradient-based Fine-Tuning through Pre-trained Model Regularization

DFReg: A Physics-Inspired Framework for Global Weight Distribution Regularization in Neural Networks

Beyond Low-Rank Tuning: Model Prior-Guided Rank Allocation for Effective Transfer in Low-Data and Large-Gap Regimes

LoRA Fine-Tuning Without GPUs: A CPU-Efficient Meta-Generation Framework for LLMs

Automatic Rank Determination for Low-Rank Adaptation via Submodular Function Maximization

DoMIX: An Efficient Framework for Exploiting Domain Knowledge in Fine-Tuning

Continual Gradient Low-Rank Projection Fine-Tuning for LLMs

Built with on top of