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...
SMART: Robust and Efficient Fine-Tuning for Pre-trainedNatural Language Models through Principled RegularizedOptimization Smoothness-inducing Adversarial Regularization fine-tunning的优化如下 是fine-tunning参数 是Smoothness-inducing Adversarial正则项 就是描述两个分布相似度的 如果是回归模型就把上面的 改...
Towards Robust Low-Resource Fine-Tuning with Multi-View Compressed Representations论文笔记 柏油无头人 北京邮电大学 计算机科学技术博士在读5 人赞同了该文章 论文链接:arxiv.org/pdf/2211.0879本文提到源码随后会公开 Abstract 由于大量的参数规模,在小样本场景下,微调预训练语言模型很容易导致过拟合。本文提出一...
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: 160 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...
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 ...
67 Chen2020Adversarial Adversarial Robustness: From Self-Supervised Pre-Training to Fine-Tuning 86.04% 51.56% ResNet-50 (3x ensemble) CVPR 2020 68 Chen2020Efficient Efficient Robust Training via Backward Smoothing 85.32% 51.12% WideResNet-34-10 arXiv, Oct 2020 69 Addepalli2021Towards_RN18 Scalin...
《Aligning Modalities in Vision Large Language Models via Preference Fine-tuning》(2024) GitHub: github.com/YiyangZhou/POVID [fig1]《T-Stitch: Accelerating Sampling in Pre-trained Diffusion Models with Trajectory Stitching》(2024) GitHub: github.com/NVlabs/T-Stitch [fig2]...