论文解读 - 随笔分类(第2页) - 图神经网络

论文解读（ECACL）《ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation》

摘要：Note：[ wechat：Y466551 | 付费咨询，非诚勿扰 ] 论文信息论文标题：ECACL: A Holistic Framework for Semi-Supervised Domain Adaptation论文作者：Kai Li, Chang Liu, Handong Zhao, Y 阅读全文

posted @ 2023-08-16 22:09 图神经网络阅读(49) 评论(0) 推荐(0) 编辑

论文解读（SentiX）《SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis》

摘要：Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息论文标题：SentiX: A Sentiment-Aware Pre-Trained Model for Cross-Domain Sentiment Analysis论文作者：Jie Zhou, Junfeng T 阅读全文

posted @ 2023-08-15 02:08 图神经网络阅读(302) 评论(0) 推荐(0) 编辑

论文解读（TAT）《 Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers》

摘要：Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息论文标题：Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers论文作者：Hong Liu, Mingsh 阅读全文

posted @ 2023-08-12 15:54 图神经网络阅读(291) 评论(0) 推荐(1) 编辑

论文解读（SimGCL）《Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation》

摘要：Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息论文标题：Are Graph Augmentations Necessary? Simple Graph Contrastive Learning for Recommendation论文作者：Junliang Yu 阅读全文

posted @ 2023-08-11 21:13 图神经网络阅读(733) 评论(0) 推荐(0) 编辑

论文解读（LightGCL）《LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation》

摘要：Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息论文标题：LightGCL: Simple Yet Effective Graph Contrastive Learning for Recommendation论文作者：Cai, Xuheng and Huang, 阅读全文

posted @ 2023-08-08 17:50 图神经网络阅读(448) 评论(0) 推荐(0) 编辑

论文解读（AAD）《Knowledge distillation for BERT unsupervised domain adaptation》

摘要：Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息论文标题：Knowledge distillation for BERT unsupervised domain adaptation论文作者：Minho Ryu、Geonseok Lee、Kichun Lee论文来阅读全文

posted @ 2023-08-07 01:57 图神经网络阅读(112) 评论(0) 推荐(0) 编辑

论文解读（APCA）《Adaptive prototype and consistency alignment for semi-supervised domain adaptation》

摘要：[ Wechat：Y466551 | 付费咨询，非诚勿扰 ] 论文信息论文标题：Adaptive prototype and consistency alignment for semi-supervised domain adaptation论文作者：Jihong Ouyang、Zhengjie 阅读全文

posted @ 2023-08-02 17:25 图神经网络阅读(110) 评论(0) 推荐(0) 编辑

论文解读（CAT）《Cluster Alignment with a Teacher for Unsupervised Domain Adaptation》

摘要：Note：[ wechat：Y466551 | 付费咨询，非诚勿扰 ] 论文信息论文标题：Cluster Alignment with a Teacher for Unsupervised Domain Adaptation论文作者：Zhijie Deng, Yucen Luo, Jun Zhu论阅读全文

posted @ 2023-08-02 10:39 图神经网络阅读(91) 评论(0) 推荐(0) 编辑

论文解读《Mixup for Node and Graph Classification》

摘要：论文信息论文标题：Mixup for Node and Graph Classification论文作者：Yiwei Wang、Wei Wang论文来源：WWW 2021论文地址：download 论文代码：download视屏讲解：click 1 介绍阅读全文

posted @ 2023-05-07 16:46 图神经网络阅读(122) 评论(0) 推荐(0) 编辑

迁移学习（VMT）《Virtual Mixup Training for Unsupervised Domain Adaptation》

摘要：论文信息论文标题：Virtual Mixup Training for Unsupervised Domain Adaptation论文作者：Takeru Miyato, S. Maeda, Masanori Koyama, S. Ishii论文来源：2019 CVPR论文地址：download 阅读全文

posted @ 2023-05-02 16:22 图神经网络阅读(55) 评论(0) 推荐(0) 编辑

论文解读（VAT）《Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning》

摘要：Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息论文标题：Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning论文作者：T 阅读全文

posted @ 2023-04-22 21:02 图神经网络阅读(252) 评论(0) 推荐(0) 编辑

论文解读《Do We Need Zero Training Loss After Achieving Zero Training Error?》

摘要：论文信息论文标题：Do We Need Zero Training Loss After Achieving Zero Training Error?论文作者：Takashi Ishida, I. Yamane, Tomoya Sakai, Gang Niu, M. Sugiyama论文来源：20 阅读全文

posted @ 2023-04-22 16:28 图神经网络阅读(93) 评论(0) 推荐(0) 编辑

论文解读（FGSM）《Explaining and Harnessing Adversarial Examples》

摘要：论文信息论文标题：Explaining and Harnessing Adversarial Examples论文作者：Ian J. Goodfellow, Jonathon Shlens, Christian Szegedy论文来源：ICLR 2015论文地址：download 论文代码：dow 阅读全文

posted @ 2023-04-21 20:46 图神经网络阅读(115) 评论(0) 推荐(0) 编辑

论文解读《Automatically discovering and learning new visual categories with ranking statistics》

摘要：论文信息论文标题：Automatically discovering and learning new visual categories with ranking statistics论文作者：K. Han, Sylvestre-Alvise Rebuffi, Sébastien Ehrhard 阅读全文

posted @ 2023-04-17 20:34 图神经网络阅读(41) 评论(0) 推荐(0) 编辑

迁移学习《Boosting Generalized Few-Shot Learning by Scattering Intra-Class Distribution》

该文被密码保护。

posted @ 2023-04-17 17:19 图神经网络阅读(2) 评论(0) 推荐(0) 编辑

论文解读（PAWS）《Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples》

摘要：论文信息论文标题：Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples论文作者：Mahmoud Assran, Mathi 阅读全文

posted @ 2023-04-17 11:18 图神经网络阅读(141) 评论(0) 推荐(0) 编辑

论文解读《Adversarial training methods for semi-supervised text classification》

摘要：论文信息论文标题：Adversarial training methods for semi-supervised text classification论文作者：Taekyung Kim论文来源：ICLR 2017论文地址：download 论文代码：download视屏讲解：click 1 背阅读全文

posted @ 2023-04-12 23:13 图神经网络阅读(232) 评论(0) 推荐(1) 编辑

论文解读（Moco v3）《An Empirical Study of Training Self-Supervised Vision Transformers》

摘要：论文信息论文标题：Improved Baselines with Momentum Contrastive Learning论文作者：Xinlei Chen, Saining Xie, Kaiming He论文来源：2021 ICCV论文地址：download 论文代码：download引用次数：阅读全文

posted @ 2023-03-21 15:58 图神经网络阅读(153) 评论(0) 推荐(0) 编辑

论文解读《Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning》

摘要：论文信息论文标题：Modeling Discriminative Representations for Out-of-Domain Detection with Supervised Contrastive Learning论文作者：Zhiyuan Zeng, Keqing He, Yuanme 阅读全文

posted @ 2023-03-19 23:08 图神经网络阅读(104) 评论(0) 推荐(0) 编辑

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