论文的核心内容是提出了一种名为WiSE-FT(Weight-space ensembling for Fine-tuning)的方法,用于在保持零样本(zero-shot)模型的鲁棒性的同时,对其进行微调(fine-tuning)以提高在特定目标分布上的准确性。零样本模型,如CLIP或ALIGN,在没有针对特定数据集进行微调的情况下,能够在一系列数据分布上保持一致的准确性。然而...
robust fine-tuning of zero-shot models "Robust fine-tuning of zero-shot models"是指对零样本模型进行稳健的微调。在机器学习中,零样本学习是指模型在没有见过特定任务的数据情况下,能够对该任务进行推断或预测。 在零样本学习中,通常使用预训练的模型,然后在新任务上进行微调,以适应特定的任务。然而,由于新...
Better Robustness by More Coverage: Adversarial and Mixup Data Augmentation for Robust Finetuning 马东什么 算法工程师5 人赞同了该文章 目录 收起 对抗学习和数据增强,两个世界的交汇 摘要 介绍 方法 对抗性数据增强 MIXUP AMDA 总结 对抗学习和数据增强,两个世界的交汇 摘要 预训练语言模型 (PLM) ...
Large pre-trained models such as CLIP or ALIGN offer consistent accuracy across a range of data distributions when performing zero-shot inference (i.e., without fine-tuning on a specific dataset). Although existing fine-tuning methods substantially improve accuracy on a given target distribution, ...
To solve this problem, we propose Context-Aware Robust Fine-tuning (CAR-FT). CAR-FT regularizes the model during fine-tuning to capture the context information. Specifically, we use zero-shot prompt weights to get the context distribution contained in the image. By minimizing the Kullback...
Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning 来自 arXiv.org 喜欢 0 阅读量: 150 作者:C Si,Z Zhang,F Qi,Z Liu,M Sun 摘要: Pre-trained language models (PLMs) fail miserably on adversarial attacks. To improve the robustness, adversarial...
It was found that by varying this parameter either the precision of the controller or the frequency of the necessary adaptive tuning can be improved. This statement is substantiated by simulations. 展开 关键词: adaptive control control system synthesis nonlinear control systems robust control stability...
BLEURT -pre:不做Pre-Training on Synthetic Data image.png image.png image.png Data-to-Text 评估:语义,语法,流畅度 image.png 备注: Pre-Training on Synthetic Data不是必要的,可以直接对Bert进行fine-tuning,但是加了这个预训练模型,模型效果好很多。
This article addresses the design procedure and numerical validation of a robust fuzzy logic-based fine-tuning approach devised to enhance load frequency control capabilities in multi-area power systems. The founded robust fuzzy logic-based fine-tuning approach is intended for judicial parameter tuning ...
3 Fine-Tuning BERT for Quality Evaluation 考虑到可用的少量评估数据,很自然地利用无监督表示来完成此任务。 在我们的模型中,我们使用BERT(来自变压器的双向编码器表示)(Devlin等人,2019),这是一种无监督的技术,可学习文本序列的上下文表示。 给定x和x〜,BERT是一个Transformer(Vaswani et al。,2017),它返回上...