semi-supervised classification adversarial supervised

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更新时间 2024-01-12

Supervised Machine Learning : Regression and Classification

The course is available at : Supervised Machine Learning: Regression and Classification - Week 1: Introduction to Machine Learning - Week 1 | Coursera ......

Classification Supervised Regression Learning Machine更新时间 2023-12-28

论文阅读-Self-supervised and Interpretable Data Cleaning with Sequence Generative Adversarial Networks

1. GARF 简介代码地址：https://github.com/PJinfeng/Garf-master 基于 SeqGAN 提出了一种自监督、数据驱动的数据清洗框架——GARF。 GARF 的数据清洗分为两个步骤：规则生成 (Rule generation with SeqGAN)：利用 ......

Self-supervised Interpretable Adversarial Generative supervised更新时间 2023-12-20

sans sec 565 Red Team Operations and Adversary Emulation - 红队运营和对手仿真之 565.1 Lab 1.4：奖金！用户名枚举和密码喷射

565.1 Lab 1.4：用户名枚举和密码喷射目标用户名枚举以发现其他有效用户使用已知密码对新发现的账户进行喷洒本实验室模拟的 TTP T1594 - Search Victim-Owned Websites T1078 - Valid Accounts T1087.003 - Accou ......

红队 Operations 奖金 565 Adversary更新时间 2023-12-20

sans sec 565 Red Team Operations and Adversary Emulation - 红队运营和对手仿真之 565.1 Lab 1.3：侦察和密码攻击

sans sec 565 Red Team Operations and Adversary Emulation - 红队运营和对手仿真之 565.1 Lab 1.3：侦察和密码攻击目标通过分析 Draconem.io 网站进行侦察确定密码攻击的目标对象通过收集电子邮件地址发现有效的用户名 ......

红队 Operations 565 Adversary Emulation更新时间 2023-12-20

《Learning Transferable Visual Models From Natural Language Supervision》论文学习

一、Abstract 最先进的计算机视觉系统被训练用以预测一组预定的固定目标类别。这种受限的监督方式限制了它们的通用性和可用性，因为需要额外的标记数据来指定任何新的视觉概念。因此，直接从关于图像的原始描述文本中学习是一个有希望的替代方法，它利用了更广泛的因特网监督来源。我们证明了预测哪个标题与哪张 ......

Transferable Supervision Learning Language Natural更新时间 2023-12-20

《Progressive Learning of Category-Consistent Multi-Granularity Features for Fine-Grained Visual Classification》阅读笔记

论文标题《Progressive Learning of Category-Consistent Multi-Granularity Features for Fine-Grained Visual Classification》细粒度视觉分类中类别一致多粒度特征的渐进学习作者 Ruoyi D ......

Category-Consistent Multi-Granularity Classification Fine-Grained Progressive更新时间 2023-12-15

sans sec 564 Red Team Operations and Adversary Emulation - 红队运营和对手仿真

564.1 红队演习介绍与规划混乱的术语定义：不需要知道这些词语的分别含义，只需要知道你在搞渗透 • Ethical Hacking • Vulnerability Scanning • Vulnerability Assessment（SEC460: Enterprise Threat and ......

红队 Operations Adversary Emulation 对手更新时间 2023-12-13

Fine-grained Visual Classification with High-temperature Refinement and Background Suppression

摘要细粒度视觉分类是一项具有挑战性的任务，因为类别之间的相似性很高，单个类别中数据之间的差异不同。为了应对这些挑战，以前的策略侧重于定位类别之间的细微差异并理解其中的判别特征。然而，背景还提供了重要信息，可以告诉模型哪些特征对于分类是不必要的甚至有害，并且过于依赖细微特征的模型可能会忽略全局特征和 ......

High-temperature Classification Fine-grained Suppression temperature更新时间 2023-12-05

[论文速览] Randomized Quantization@ A Generic Augmentation for Data Agnostic Self-supervised Learning

Pre title: Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning accepted: ICCV 2023 paper: https://arxiv.org/abs ......

Self-supervised Quantization Augmentation Randomized supervised更新时间 2023-12-04

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 时序更新时间 2023-11-28

Multivariate time series classification pytorch lstm

import torch import torch.nn as nn import torch.optim as optim import numpy as np from sklearn.model_selection import train_test_split # 生成示例数据 np.ran ......

classification Multivariate pytorch series time更新时间 2023-11-27

Kaggle:Otto Group Classification

Kaggle:Otto Group Classification 数据处理导入相应的包之后，从csv文件中读取数据，指定id列为index列。本身id列也不携带预测信息。同时将训练数据和测试数据拼接在一起。 train_data = pd.read_csv("dataset/train.csv", ......

Classification Kaggle Group Otto更新时间 2023-11-24

什么是人工智能领域的 SFT - Supervised Finetuning

在人工智能（AI）领域，Supervised Finetuning 是一个重要的概念。它涉及到在预训练模型的基础上，利用有标签的数据进行微调，以适应特定的任务或领域。这个过程可以被视为在更广泛的知识基础上进行特定任务的训练，从而在新任务上获得更好的性能。 Supervised Finetuning ......

人工智能 Finetuning Supervised 人工领域更新时间 2023-11-18

《A Survey on Deep Learning-based Fine-grained Object Classification and Semantic Segmentation》阅读笔记

论文标题《A Survey on Deep Learning-based Fine-grained Object Classification and Semantic Segmentation》基于深度学习的细粒度对象分类和语义分割的综述为什么是 “Object” 而不是 “image” 作 ......

Classification Learning-based Fine-grained Segmentation Learning更新时间 2023-11-17

CART（Classification and Regression Trees）

CART（Classification and Regression Trees）是一种常用的决策树算法，既可以用于分类问题，也可以用于回归问题。CART算法由Breiman等人于1984年提出，是一种基于递归二分划分的贪婪算法。以下是对CART算法的详细解释： 1. 决策树的构建过程： CART算 ......

Classification Regression Trees CART and更新时间 2023-11-16

基于时间频率一致性对时间序列进行自监督对比预训练《Self-Supervised Contrastive Pre-Training for Time Series via Time-Frequency Consistency》(时序、时频一致性、对比学习)

2023年11月10日，今天看一篇论文，现在17:34，说实话，想摆烂休息，不想看，可还是要看，拴Q。论文：Self-Supervised Contrastive Pre-Training for Time Series via Time-Frequency Consistency 或者是：Sel ......

一致性时间序列时间时序 Time更新时间 2023-11-14

study of 'Missing data imputation framework for bridge structural health monitoring based on slim generative adversarial networks'

the Stochastic Gradient Descent (SGD):为了提高鲁棒性，SGAIN框架的优化器采用了随机梯度下降（SGD）一，SGAIN框架有两个重要目的：鉴别器D的目的是最大化正确预测M矩阵的概率；生成器的目的是最小化D预测M矩阵的概率。此外，利用反向传播算法对发生器和鉴别器 ......

adversarial imputation generative monitoring structural更新时间 2023-11-14

共140篇 :1/5页 首页上一页1234下一页尾页

JZTXT

semi-supervised classification adversarial supervised

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

Supervised Machine Learning : Regression and Classification

论文阅读-Self-supervised and Interpretable Data Cleaning with Sequence Generative Adversarial Networks

sans sec 565 Red Team Operations and Adversary Emulation - 红队运营和对手仿真之 565.1 Lab 1.4：奖金！用户名枚举和密码喷射

sans sec 565 Red Team Operations and Adversary Emulation - 红队运营和对手仿真之 565.1 Lab 1.3：侦察和密码攻击

《Learning Transferable Visual Models From Natural Language Supervision》论文学习

《Progressive Learning of Category-Consistent Multi-Granularity Features for Fine-Grained Visual Classification》阅读笔记

sans sec 564 Red Team Operations and Adversary Emulation - 红队运营和对手仿真

Fine-grained Visual Classification with High-temperature Refinement and Background Suppression

[论文速览] Randomized Quantization@ A Generic Augmentation for Data Agnostic Self-supervised Learning

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)

Multivariate time series classification pytorch lstm

Kaggle:Otto Group Classification

什么是人工智能领域的 SFT - Supervised Finetuning

《A Survey on Deep Learning-based Fine-grained Object Classification and Semantic Segmentation》阅读笔记

CART（Classification and Regression Trees）

基于时间频率一致性对时间序列进行自监督对比预训练《Self-Supervised Contrastive Pre-Training for Time Series via Time-Frequency Consistency》(时序、时频一致性、对比学习)

study of 'Missing data imputation framework for bridge structural health monitoring based on slim generative adversarial networks'

ST-SSL: 用于交通流量预测的时空自监督学习《Spatio-Temporal Self-Supervised Learning for Traffic Flow Prediction》(交通流量预测、自监督)

论文：Ultra Fast Deep Lane Detection with Hybrid Anchor Driven Ordinal Classification-基于anchor方法

神经网络基础篇：详解二分类(Binary Classification)

【Kaggle】Spam/Ham Email Classification

为什么我们需要不断的开发不同的机器学习模型 —— Do we Need Hundreds of Classifiers to Solve Real World Classification Problems?

Graph Laplacian for Semi-Supervised Learning

Implicit Autoencoder for Point-Cloud Self-Supervised Representation Learning论文阅读

(2023年新疆大学、中科院等点云分类最新综述) Deep learning-based 3D point cloud classification: A systematic survey and outlook

GAN（生成对抗网络，Generative Adversarial Network）

AlexNet模型：ImageNet Classification with Deep Convolutional Neural Networks

Chinese-Text-Classification-PyTorch

《ImageNet Classification with Deep Convolutional Neural Networks》阅读笔记