MLAE: Masked LoRA Experts for Visual Parameter-Efficient Fine-Tuning

TL;DR

MLAE introduces a masking-based approach to enhance parameter-efficient fine-tuning of large models by increasing diversity and independence among low-rank matrices, leading to state-of-the-art results with fewer parameters.

Contribution

The paper proposes Masked LoRA Experts (MLAE), a novel method that decomposes low-rank matrices into independent experts and uses masking to improve diversity and performance.

Findings

Achieves 78.8% accuracy on VTAB-1k benchmark.

Achieves 90.9% accuracy on FGVC benchmark.

Surpasses previous SOTA by 0.8% with fewer parameters.

Abstract

In response to the challenges posed by the extensive parameter updates required for full fine-tuning of large-scale pre-trained models, parameter-efficient fine-tuning (PEFT) methods, exemplified by Low-Rank Adaptation (LoRA), have emerged. LoRA simplifies the fine-tuning process but may still struggle with a certain level of redundancy in low-rank matrices and limited effectiveness from merely increasing their rank. To address these issues, a natural idea is to enhance the independence and diversity of the learning process for the low-rank matrices. Therefore, we propose Masked LoRA Experts (MLAE), an innovative approach that applies the concept of masking to visual PEFT. Our method incorporates a cellular decomposition strategy that transforms a low-rank matrix into independent rank-1 submatrices, or "experts", thus enhancing independence. Additionally, we introduce a binary mask matrix that selectively activates these experts during training to promote more diverse and anisotropic learning, based on expert-level dropout strategies. Our investigations reveal that this selective activation not only enhances performance but also fosters a more diverse acquisition of knowledge with a marked decrease in parameter similarity among MLAE, significantly boosting the quality of the model. Remarkably, MLAE achieves new state-of-the-art (SOTA) performance with an average accuracy score of 78.8% on the VTAB-1k benchmark and 90.9% on the FGVC benchmark, surpassing the previous SOTA result by an average of 0.8% on both benchmarks with approximately half parameters. Our code is available at https://github.com/jie040109/MLAE.