fixmatch semi-supervised consistency simplifying
Cycle GAN：Unpaired Image-to-Image Translation using Cycle-Consistent Adversarial Networks
paper：https://arxiv.org/pdf/1703.10593.pdf [2017] code 参考： https://github.com/junyanz/pytorch-CycleGAN-and-pix2pix https://zhuanlan.zhihu.com/p/792211 ......
论文解读（VAT）《Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning》
论文信息 论文标题：Virtual Adversarial Training: A Regularization Method for Supervised and Semi-Supervised Learning论文作者：Takeru Miyato, S. Maeda, Masanori Koya ......
论文解读（PAWS）《Semi-Supervised Learning of Visual Features by Non-Parametrically Predicting View Assignments with Support Samples》
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 ......
迁移学习（CLDA）《CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation》
论文信息 论文标题：CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation论文作者：Ankit Singh论文来源：NeurIPS 2021论文地址：download 论文代码：download视屏讲解：click 1 简介 ......
Deeper Insights into Graph Convolutional Networks for Semi-Supervised Learning
Li Q., Han Z. and Wu X. Deeper insights into graph convolutional networks for semi-supervised learning. AAAI, 2018. 概 本文分析了 GCN 的实际上就是一种 Smoothing, 但是 ......
论文解读（ FGSM）《Adversarial training methods for semi-supervised text classification》
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 ......
迁移学习（）《Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation》
论文信息 论文标题：Attract, Perturb, and Explore: Learning a Feature Alignment Network for Semi-supervised Domain Adaptation论文作者：Taekyung Kim论文来源：2020 ECCV论文地址 ......
迁移学习《Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification》
论文信息 论文标题：Cluster-Guided Semi-Supervised Domain Adaptation for Imbalanced Medical Image Classification论文作者：S. Harada, Ryoma Bise, Kengo Araki论文来源：ArXi ......
迁移学习（SPI）《Semi-Supervised Domain Adaptation by Similarity based Pseudo-label Injection》
论文信息 论文标题：Semi-Supervised Domain Adaptation by Similarity based Pseudo-label Injection论文作者：Abhay Rawat, Isha Dua, Saurav Gupta, Rahul Tallamraju 论文来源： ......
迁移学习《Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks》
论文信息 论文标题：Pseudo-Label : The Simple and Efficient Semi-Supervised Learning Method for Deep Neural Networks论文作者：Dong-Hyun Lee论文来源：2013——ICML论文地址：downlo ......
28.MGR 重要参数group_replication_consistency
参数group_replication_consistency共 5 个值可选： 1. EVENTUAL：确保最终一致性，并不能保证数据实时同步。(MySQL 8.0.14 之前只有这一个选项) 2. BEFORE：确保本地强一致性，并不保证其他节点数据实时同步。 3. AFTER：确保全局强一致性 ......
CSCI-1200 Simplified B+ Trees
CSCI-1200 Data Structures — Spring 2023Homework 8 — Simplified B+ TreesIn this assignment we will be implementing a partial and modified version of B+ ......
小样本利器5. 半监督集各家所长：MixMatch，MixText，UDA，FixMatch
在前面章节中，我们介绍了几种半监督方案包括一致性正则，FGM对抗，最小熵原则，mixup增强。MixMatch则是集各家所长，把上述方案中的SOTA都融合在一起实现了1+1+1>3的效果。我们以MixMatch为基准，一并介绍几种衍生方案MixText，UDA，FixMatch ......
迁移学习（MixMatch）《MixMatch: A Holistic Approach to Semi-Supervised Learning》
论文信息 论文标题：MixMatch: A Holistic Approach to Semi-Supervised Learning论文作者：David Berthelot, Nicholas Carlini, Ian Goodfellow, Nicolas Papernot, Avital Ol ......