Feature Distillation With Guided Adversarial Contrastive Learning

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Feature Distillation With Guided Adversarial Contrastive Learning

目录概主要内容 reweight 拟合概率实验的细节疑问 Bai T., Chen J., Zhao J., Wen B., Jiang X., Kot A. Feature Distillation With Guided Adversarial Contrastive Learning. arXiv preprint arXiv 2009.09922, 2020. 概本文是通过固定教师网络(具有鲁棒性), 让学生网络去学习教师网络的鲁棒特征. 相较于一般的distillation…

Robust Pre-Training by Adversarial Contrastive Learning

目录概主要内容代码 Jiang Z., Chen T., Chen T. & Wang Z. Robust Pre-Training by Adversarial Contrastive Learning. NIPS, 2020. 概本文介绍了一种利用对比学习进行对抗预训练的方法. 主要内容思想是很简单的, 就是利用对比学习进行训练(样本的augumentation多一个\(\delta\)), 然后再通过此方法训练得到的参数进行finetune. 比较特别的是, 有三种预训练的方案:…

谣言检测（GACL）《Rumor Detection on Social Media with Graph Adversarial Contrastive Learning》

论文信息论文标题:Rumor Detection on Social Media with Graph AdversarialContrastive Learning论文作者:Tiening Sun.Zhong Qian.Sujun Dong论文来源:2022, WWW论文地址:download论文代码:download Abstract 尽管基于GNN的方法在谣言检测领域取得了一些成功,但是这些基于交叉熵损失的方法常常导致泛化能力差,并且缺乏对一些带有噪声的或者对抗性的样本的鲁棒性,尤其是一…

Adversarial Self-Supervised Contrastive Learning

目录概主要内容 Linear Part 代码 Kim M., Tack J. & Hwang S. Adversarial Self-Supervised Contrastive Learning. In Advances in Neural Information Processing Systems, 2020. 概这篇文章提出了对比学习结合adversarial training的一个思路. 主要内容对比学习的强大之处在于正负样本对的构造, 一个结合adversarial trai…

Remote Sensing Images Semantic Segmentation with General Remote Sensing Vision Model via a Self-Supervised Contrastive Learning Method

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ICLR2021对比学习（Contrastive Learning）NLP领域论文进展梳理

本文首发于微信公众号「对白的算法屋」,来一起学AI叭大家好,卷王们and懂王们好,我是对白. 本次我挑选了ICLR2021中NLP领域下的六篇文章进行解读,包含了文本生成.自然语言理解.预训练语言模型训练和去偏.以及文本匹配和文本检索.从这些论文的思想中借鉴了一些idea用于公司自身的业务中,最终起到了一个不错的效果. 1.Contrastive Learning with Adversarial Perturbations for Conditional Text Generation 任务…

论文解读（S^3-CL）《Structural and Semantic Contrastive Learning for Self-supervised Node Representation Learning》

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论文解读（SimGRACE）《SimGRACE: A Simple Framework for Graph Contrastive Learning without Data Augmentation》

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论文解读（GGD）《Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination》

论文信息论文标题:Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination论文作者:Yizhen Zheng, Shirui Pan, Vincent Cs Lee, Yu Zheng, Philip S. Yu论文来源:2022,NeurIPS论文地址:download 论文代码:download 1 Introduction…

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本文记录了博主阅读ICCV2019一篇关于主动学习论文的笔记,第一篇博客,以后持续更新哈哈论文题目:<Variational AdVersarial Active Learning> 原文地址:https://arxiv.org/pdf/1904.00370 开源地址:https://github.com/sinhasam/vaal ·摘要主动学习旨在形成有效标记的算法,通过采样最有代表性的查询结果去使用标注专家标记.本文描述了一种基于池的半监督主动学习算法,以对抗的方式学习采样机制.通过…