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...
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...
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 ...
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 来自专栏 · 论文阅读笔记 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...
To alleviate this, we propose Noise-Robust Fine-tuning (NRF) which tries to extract clean textual information from a possibly noisy target-language input with the guidance of its source-language counterpart. Besides, contrastive learning involving different modalities are performed to strengthen the ...
distributionally robust finetuning bert for covariate drift in ... [Paper] adversarial adaptation of synthetic or stale data [Paper] semi-supervised domain adaptation for dependency parsing ... [Paper] joint and conditional estimation of tagging and parsing models [Paper] measure and improve ...
We finetuned mplug-owl on 8 V100. If you meet any questions when implement on V100, feel free to let me know! 2. Download the Checkpoint First download the checkpoint of mplug-owl fromlinkand the trained lora model weight fromhere. ...
Robust Fine-tuning of Deep Neural Networks with Hessian-based Generalization Guarantees Data Determines Distributional Robustness in Contrastive Language Image Pre-training Perfectly Balanced: Improving Transfer and Robustness of Supervised Contrastive Learning GSmooth: Certified Robustness against Semantic Transform...
depth_fine_tuning.py dynamic_mask_generation.py flow.py main.py optical_flow_homography.py params.py pose_optimization.py process.py video.py README Code of conduct License [CVPR 2021] Robust Consistent Video Depth Estimation This repository contains Python and C++ implementation of Robust Consisten...