Efficient finetuning of pretrained language transformers is becoming
increasingly prevalent for solving natural language processing tasks. While
effective, it can still require a large number of tunable parameters. This can
be a drawback for low-resource applications and training with
differential-privacy constraints, where excessive noise may be introduced
during finetuning. To this end, we propose a novel language transformer
finetuning strategy that introduces task-specific parameters in multiple
transformer layers. These parameters are derived from fixed random projections
of a single trainable vector, enabling finetuning with significantly fewer
parameters while maintaining performance. We achieve within 5% of full
finetuning performance on GLUE tasks with as few as 4,100 parameters per task,
outperforming other parameter-efficient finetuning approaches that use a
similar number of per-task parameters. Besides, the random projections can be
precomputed at inference, avoiding additional computational latency. All these
make our method particularly appealing for low-resource applications. Finally,
our method achieves the best or comparable utility compared to several recent
finetuning methods when training with the same privacy constraints,
underscoring its effectiveness and potential real-world impact.

本研究提出了一种新的语言转换微调策略，它在多个转换器层中引入了特定于任务的参数，这些参数是来自单一可训练向量的固定随机投影，使微调具有明显更少的参数，从而实现了 “低资源应用程序及带隐私约束条件训练” 条件下的有效性和性能。

联合重新参数化的多层适应性用于高效和私密调整

Jointly Reparametrized Multi-Layer Adaptation for Efficient and Private  Tuning

We present DreamPose, a diffusion-based method for generating animated
fashion videos from still images. Given an image and a sequence of human body
poses, our method synthesizes a video containing both human and fabric motion.
To achieve this, we transform a pretrained text-to-image model (Stable
Diffusion) into a pose-and-image guided video synthesis model, using a novel
finetuning strategy, a set of architectural changes to support the added
conditioning signals, and techniques to encourage temporal consistency. We
fine-tune on a collection of fashion videos from the UBC Fashion dataset. We
evaluate our method on a variety of clothing styles and poses, and demonstrate
that our method produces state-of-the-art results on fashion video animation.
Video results are available on our project page.

DreamPose 是一种基于扩散的方法，用于从静止图像生成动画时装视频，通过使用一组新颖的微调策略、建筑变化和技术来实现姿势与图像引导的视频合成模型，并鼓励时间的一致性，通过对 UBC 时尚数据集的时装视频进行微调来对我们的方法进行评估，证明我们的方法在时装视频动画方面取得了最先进的效果。