PELA: Learning Parameter-Efficient Models with Low-Rank Approximation
This addresses efficiency challenges for deploying large models in resource-limited settings, though it is incremental as it builds on existing parameter-efficient fine-tuning methods.
The paper tackles the problem of efficiently applying pre-trained large models to downstream tasks under resource constraints by introducing an intermediate pre-training stage with low-rank approximation, achieving a performance decrease of only ~0.6 points while reducing parameter size by 1/3 to 2/3.
Applying a pre-trained large model to downstream tasks is prohibitive under resource-constrained conditions. Recent dominant approaches for addressing efficiency issues involve adding a few learnable parameters to the fixed backbone model. This strategy, however, leads to more challenges in loading large models for downstream fine-tuning with limited resources. In this paper, we propose a novel method for increasing the parameter efficiency of pre-trained models by introducing an intermediate pre-training stage. To this end, we first employ low-rank approximation to compress the original large model and then devise a feature distillation module and a weight perturbation regularization module. These modules are specifically designed to enhance the low-rank model. In particular, we update only the low-rank model while freezing the backbone parameters during pre-training. This allows for direct and efficient utilization of the low-rank model for downstream fine-tuning tasks. The proposed method achieves both efficiencies in terms of required parameters and computation time while maintaining comparable results with minimal modifications to the backbone architecture. Specifically, when applied to three vision-only and one vision-language Transformer models, our approach often demonstrates a merely $\sim$0.6 point decrease in performance while reducing the original parameter size by 1/3 to 2/3.