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) ...
Many state-of-the-art models are first pre-trained on a large text corpus and then fine-tuned on downstream tasks. However, due to limited data resources from downstream tasks and the extremely high complexity of pre-trained models, aggressive fine-tuning often causes the fine-tuned model to...
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, ...
Better Robustness by More Coverage: Adversarial Training with Mixup Augmentation for Robust Fine-tuning 来自 arXiv.org 喜欢 0 阅读量: 156 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...
Fine-Tuning BERT for Quality Evaluation 将生成的文本和参考文本一同输入到BERT模型中,产生向量: 使用[CLS]的向量,添加线性层,得到: 模型得分和人工打分的loss: Experiments Translation 模型在WMT17年到19年评估效果如下: BLEURTbase:Bert_base BLEURT:Bert_large ...
We propose a general frameworkVirtual Data Augmentation (VDA)for robustly fine-tuning Pre-trained Language Models for downstream tasks. Our VDA utilizes a masked language model with Gaussian noise to augment virtual examples for improving the robustness, and also adopts regularized training to further ...
66 Chen2020Adversarial Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning 86.04% 51.56% ResNet-50 (3x ensemble) CVPR 2020 67 Chen2020Efficient Efficient Robust Training via Backward Smoothing 85.32% 51.12% WideResNet-34-10 arXiv, Oct 2020 68 Addepalli2021Towards_RN18 Scalin...