Efficient fine-tuning plays a fundamental role in modern large models, with
low-rank adaptation emerging as a particularly promising approach. However, the
existing variants of LoRA are hampered by limited expressiveness, a tendency to
overfit, and sensitivity to hyperparameter settings. This paper presents LoRA
Slow Cascade Learning (LoRASC), an innovative technique designed to enhance
LoRA's expressiveness and generalization capabilities while preserving its
training efficiency. Our approach augments expressiveness through a cascaded
learning strategy that enables a mixture-of-low-rank adaptation, thereby
increasing the model's ability to capture complex patterns. Additionally, we
introduce a slow-fast update mechanism and cascading noisy tuning to bolster
generalization. The extensive experiments on various language and vision
datasets, as well as robustness benchmarks, demonstrate that the proposed
method not only significantly outperforms existing baselines, but also
mitigates overfitting, enhances model stability, and improves OOD robustness.
Code will be release in this https URL very soon.

LoRA Slow Cascade Learning (LoRASC) is an innovative technique that enhances the expressiveness and generalization capabilities of efficient fine-tuning through a cascaded learning strategy and a slow-fast update mechanism, improving model stability, mitigating overfitting, and enhancing OOD robustness.

通过慢速级联学习实现富表达和可推广的大规模模型的低秩适应

Expressive and Generalizable Low-rank Adaptation for Large Models via  Slow Cascaded Learning

Speech-driven 3D facial animation aims at generating facial movements that
are synchronized with the driving speech, which has been widely explored
recently. Existing works mostly neglect the person-specific talking style in
generation, including facial expression and head pose styles. Several works
intend to capture the personalities by fine-tuning modules. However, limited
training data leads to the lack of vividness. In this work, we propose AdaMesh,
a novel adaptive speech-driven facial animation approach, which learns the
personalized talking style from a reference video of about 10 seconds and
generates vivid facial expressions and head poses. Specifically, we propose
mixture-of-low-rank adaptation (MoLoRA) to fine-tune the expression adapter,
which efficiently captures the facial expression style. For the personalized
pose style, we propose a pose adapter by building a discrete pose prior and
retrieving the appropriate style embedding with a semantic-aware pose style
matrix without fine-tuning. Extensive experimental results show that our
approach outperforms state-of-the-art methods, preserves the talking style in
the reference video, and generates vivid facial animation. The supplementary
video and code will be available at this https URL

通过 AdaMesh 方法，我们能够从仅有大约 10 秒的参考视频中学习个性化的说话风格，生成生动的面部表情和头部姿势，超过了现有方法并保留了参考视频中的说话风格。