526互联

non-parametrically semi-supervised parametrically

Early lameness detection in dairy cattle based on wearable gait analysis using semi-supervised LSTM-Autoencoder

一区top Computers and Electronics in Agriculture 题目：“基于半监督 LSTM-自动编码器可穿戴步态分析的奶牛早期跛行检测” (Zhang 等, 2023, p. 1) (pdf) “Early lameness detection in dairy ca ......
LSTM-Autoencoder semi-supervised Autoencoder supervised detection

软件测试/测试开发/全日制|Pytest参数化神器，pytest.mark.parametrize()使用

前言 当我们要使用pytest输入多个数据对一个功能进行测试时，如果写多个测试用例的话，那就完全体现不出通过代码来执行测试的优势了，这个时候，就轮到pytest的参数化功能闪亮登场了。所谓参数化，就是就是把测试过程中的数据提取出来，通过参数传递不同的数据来驱动用例运行。其实也就是数据驱动的概念。本文 ......
全日 全日制 软件测试 神器 parametrize

pytest框架之yaml详解以及Parametrize数据驱动

1. YAML语法详解 yaml是一个完全类似于json数据序列化格式 重点： yaml完全兼容json yaml和json，都是数据，不是语句 序列化：将内存中的对象，转为文件 反序列化：将文件内容，转为内存中的对象 文本文件（可以使用记事本进行创建，编辑，修改） 优点： 结构更加清晰 语法高级， ......
Parametrize 框架 数据 pytest yaml

CA-TCC: 半监督时间序列分类的自监督对比表征学习《Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification》(时间序列、时序表征、时间和上下文对比、对比学习、自监督学习、半监督学习、TS-TCC的扩展版)

现在是2023年11月27日，10:48，今天把这篇论文看了。 论文：Self-supervised Contrastive Representation Learning for Semi-supervised Time-Series Classification GitHub：https://g ......
时间序列 时间 序列 supervised 时序

Graph Laplacian for Semi-Supervised Learning

目录概符号说明Graph-Laplacian for SSL Streicher O. and Gilboa G. Graph laplacian for semi-supervised learning. arXiv preprint arXiv:2301.04956, 2023. 概 标题取得有 ......
Semi-Supervised Supervised Laplacian Learning Graph

G7、Semi-Supervised GAN 理论与实战

🍨 本文为🔗365天深度学习训练营 中的学习记录博客 🍖 原作者：K同学啊 🚀 文章来源：K同学的学习圈子 问题由来¶ 如果我们生成的图像是带有标签的，例如数字0-9，那为什么要鉴别器判断输入图像为真假，而不直接判断图像是0-9中的哪一个数字呢，这样的鉴别效果不是更好吗 一、SGAN理论基础 ......
Semi-Supervised Supervised 实战 理论 Semi

论文解读（CR-Match）《Revisiting Consistency Regularization for Semi-Supervised Learning》

Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息 论文标题：Revisiting Consistency Regularization for Semi-Supervised Learning论文作者：Yue Fan、Anna Kukleva、Bernt Schie ......
Semi-Supervised Regularization Consistency Revisiting Supervised

论文解读（FixMatch）《FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence》

Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息 论文标题：FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence论文作者：论文来源：2020 aRxiv论文地址： ......
FixMatch Semi-Supervised Consistency Simplifying Confidence

Graph Construction and b-Matching for Semi-Supervised Learning

目录概符号说明图的构建Graph Sparsification\(\epsilon\)-neighborhood graph\(k\)NN graph\(b\)-MatchingGraph Edge Re-Weighting Jebara T., Wang J. and Chang S. Graph ......
Semi-Supervised Construction Supervised b-Matching Matching

论文精读：带有源标签自适应的半监督域适应（Semi-Supervised Domain Adaptation with Source Label Adaptation）

# Semi-Supervised Domain Adaptation with Source Label Adaptation 具有源标签适应的半监督域适应 >[原文链接](https://openaccess.thecvf.com/content/CVPR2023/papers/Yu_Semi- ......
Adaptation Semi-Supervised Supervised 标签 Domain

pytest.mark.parametrize() 列表2

yaml文件: - - list_order - 南京 - - list_order - 北京 - - list_order - 郑州 - - list_order - 西安 代码: import json import pprint import pytest from Slience.utils ......
parametrize pytest mark

pytest.mark.parametrize() 字典

yaml文件 - action: list_order keywords: 南京 - action: list_order keywords: 郑州 - action: list_order keywords: 西安 代码: import json import pprint import pyte ......
parametrize 字典 pytest mark

pytest.mark.parametrize() 列表1

yaml文件: - 南京 - 北京 - 郑州 - 西安 代码: import json import pprint import pytest from Slience.utils.login_util import Login from Slience.utils.request_util imp ......
parametrize pytest mark

[论文阅读] Learning Semi-supervised Gaussian Mixture Model

# Learning Semi-supervised Gaussian Mixture Models for Generalized Category Discovery ## Abstract 在本文中，我们解决了广义类别发现(generalized category discovery, GCD ......
Semi-supervised supervised Learning Gaussian Mixture

[论文理解] HACK: Learning a Parametric Head and Neck Model for High-fidelity Animation

# HACK: Learning a Parametric Head and Neck Model for High-fidelity Animation 上科大发布的头和脖子精细建模的参数化模型HACK。 ## 纹理转化 由于HACK没有开源纹理基，我将FLAME开源的纹理基迁移到了HACK上，代 ......
High-fidelity Parametric Animation Learning fidelity

论文解读（KDSSDA）《Knowledge distillation for semi-supervised domain adaptation》

Note：[ wechat：Y466551 | 可加勿骚扰，付费咨询 ] 论文信息 论文标题：Knowledge distillation for semi-supervised domain adaptation论文作者：Mauricio Orbes-Arteaga, Jorge Cardoso论 ......
semi-supervised distillation adaptation supervised Knowledge

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

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 ......
ECACL Semi-Supervised Adaptation Supervised Framework

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

[ 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 ......
semi-supervised consistency adaptation supervised prototype

需要生成多条测试用例 需要装饰器@pytest.mark.parametrize 测试方法将会根据参数组合多次运行

# py 文件必须test开头 # 测试类必须Test开头 import ast import pytest from utils.handle_yaml import get_yaml from utils.handle_xls_my import get_excel_data import js ......
多条 parametrize 参数 方法 pytest

Pytest.mark.parametrize()基本用法

### Pytest.mark.parametrize()基本用法 @pytest.mark.parametrize()基本用法 数据驱动：就是把我们测试用例的数据放到excel,yaml,csv,mysql，然后通过去改变数据达到改变测试用例的执行结果。 @pytest.mark.parametr ......
parametrize Pytest mark

pytest之parametrize数据驱动

1.数据驱动 1.1 yaml文件 yaml是一种数据类型，扩展名:.yaml和.yml 作用： 配置文件：环境变量，数据库信息，用户名密码，日志格式等 测试用例：web，ui,app 语法规则： 区分大小写 通过缩进表示层级关系，一般用空格，不要使用tab键 通过#注释 字符串可以不用写引号，也可 ......
parametrize 数据 pytest

CF1656F Parametric MST 题解

为了便于解题，先对 $a$ 数组从小到大进行排序。 首先，根据定义可以得出总价值的表达式： $$ \begin{aligned} W&=\sum\limits_{(u,v)\in E}[a_ua_v+t(a_u+a_v)]\ &=\sum\limits_{(u,v)\in E}a_ua_v+t\su ......
题解 Parametric 1656F 1656 MST

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 ......
Supervised Semi-Supervised Regularization Adversarial Training

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 ......
Non-Parametrically Semi-Supervised Parametrically Assignments Predicting

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 ......
CLDA Semi-Supervised Contrastive Adaptation Supervised

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 ......
Semi-Supervised Convolutional Supervised Insights Networks

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 ......
semi-supervised classification Adversarial supervised training

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 ......
Semi-supervised Adaptation supervised Alignment Learning

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 ......
Semi-Supervised Cluster-Guided Classification Adaptation Imbalanced

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： ......
Semi-Supervised Pseudo-label Adaptation Supervised Similarity
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