Unsupervised image to-image translation via pre-trained style gan2 network
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광희 이Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 Network (https://arxiv.org/abs/2010.05713)
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For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-baidu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Dr. Ren Wu, former distinguished scientist at Baidu's Institute of Deep Learning (IDL), presents the keynote talk, "Enabling Ubiquitous Visual Intelligence Through Deep Learning," at the May 2015 Embedded Vision Summit.
Deep learning techniques have been making headlines lately in computer vision research. Using techniques inspired by the human brain, deep learning employs massive replication of simple algorithms which learn to distinguish objects through training on vast numbers of examples. Neural networks trained in this way are gaining the ability to recognize objects as accurately as humans.
Some experts believe that deep learning will transform the field of vision, enabling the widespread deployment of visual intelligence in many types of systems and applications. But there are many practical problems to be solved before this goal can be reached. For example, how can we create the massive sets of real-world images required to train neural networks? And given their massive computational requirements, how can we deploy neural networks into applications like mobile and wearable devices with tight cost and power consumption constraints?
In this talk, Ren shares an insider’s perspective on these and other critical questions related to the practical use of neural networks for vision, based on the pioneering work being conducted by his former team at Baidu.
Note 1: Regarding the ImageNet results included in this presentation, the organizers of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) have said: “Because of the violation of the regulations of the test server, these results may not be directly comparable to results obtained and reported by other teams.” (http://www.image-net.org/challenges/LSVRC/announcement-June-2-2015)
Note 2: The presenter, Ren Wu, has told the Embedded Vision Alliance that “There was some ambiguity with the rules. According to the ‘official’ interpretation of the rules, there should be no more than 52 submissions within a half year. For us, we achieved the reported results after 200 tests total within a half year. We believe there is no way to obtain any measurable gains, nor did we try to obtain any gains, from an 'extra' hundred tests as our networks have billions of parameters and are trained by tens of billions of training samples.”Using Crowdsourced Images to Create Image Recognition Models with Analytics Z...
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Review : Structure Boundary Preserving Segmentation
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Paper title : Structure Boundary Preserving Segmentation
for Medical Image with Ambiguous Boundary (CVPR2020)
Paper link : https://openaccess.thecvf.com/content_CVPR_2020/papers/Lee_Structure_Boundary_Preserving_Segmentation_for_Medical_Image_With_Ambiguous_Boundary_CVPR_2020_paper.pdf
High PSNR Based Image Steganography
In recent time, the Steganography technique is broadly used for the secret data communication. It’s an art of hiding the secret data in another objects like videos, images, videos, graphics and documents to gain the stego or steganographic object so which it’s not affected by the insertion. In this paper, we are introducing a new methodology in which security of stego-image increase by embedding even and odd part secret image into R, G, B plane of cover image using LSB and ISB technique. As we can see from the results session the value of PSNR , NCC are getting increase while the value of MSE is getting decrease.
Image analytics - A Primer
An Intro to Image Analytics. A slightly rehashed version of Computer Vision - Old Problems and New Solutions PPT
IRJET- Transformation of Realistic Images and Videos into Cartoon Images and ...
This document summarizes research on using a Generative Adversarial Network (GAN) called Cartoon GAN to transform real-world images and videos into cartoon images and videos. The researchers trained Cartoon GAN on 3000 real-world images to learn how to generate cartoon images by using content and adversarial loss functions. They were able to successfully convert both individual images and video clips into cartoon/animated versions. For video, they used the OpenCV library to divide videos into frames, pass each frame through the trained Cartoon GAN model, and then recombine the cartoonized frames into an output cartoon video. The researchers concluded that Cartoon GAN is an effective method for automatically transforming real media into cartoons and aims to improve the quality and resolution
Image Maximization Using Multi Spectral Image Fusion Technique
This paper reports a detailed study of a set of image fusion algorithms for its implementation. The paper explains the theory and implementation of the effective image fusion algorithm and the experimental results. Based on the research and development of some image quality metrics, the fusion algorithm is evaluated. The report is an image fusion algorithm that evaluates and implements image quality metrics that are used to evaluate the implementation. In this study, two different image fusion techniques have been applied to hyperspectral and low spatial resolution satellite images with high spatial and low spectral resolution images to obtain a fusion graph with increased spatial resolution Like, while keeping spectral information as much as possible. These techniques are raw component analysis
(PCA) and wavelet transform (WT) image fusion MATLAB is used to build the GUto
apply and render the results of image fusion algorithms. The subjective (visual) and objective evaluation of the fusion image has been carried out to assess the success of the method. The objective evaluation methods include correlation coefficient (CC), root mean square error (RMSE), relative global dimension synthesis error (ERGAS) The results show that the PCA method performs better on the top of the spectral information, and is less successful in increasing the spatial resolution. The WT is performed after the IHS transformation to improve the spatial resolution and is performed with respect to the preservation of the spectral information after the PCA and WT methods.
Hao_Guan_2011
This project aims to present a new method for recognizing pedestrians in image datasets. The method combines two state-of-the-art object recognition techniques: the cortex model and bag of words with spatial pyramid. The cortex model imitates the hierarchical structure of the primate visual cortex. This project improves the cortex model by training on both positive and negative examples. It also uses the outputs of the first cortex model layer instead of SIFT features. The results show this new method performs better than the cortex model alone for pedestrian recognition.
An Introduction to Computer Vision
This document provides an introduction to computer vision. It summarizes the state of the field, including popular challenges like PASCAL VOC and SRVC. It describes commonly used algorithms like SIFT for feature extraction and bag-of-words models. It also discusses machine learning methods applied to computer vision like support vector machines, randomized forests, boosting, and Viola-Jones face detection. Examples of results from applying these techniques to object classification problems are also provided.
Road signs detection using voila jone's algorithm with the help of opencv
This document provides an introduction and overview of a project to develop an automatic road sign detection system using the Viola-Jones object detection framework. It discusses the motivation for the project to address safety concerns from drivers missing road signs. The document outlines the contributions of the project, which are to train a classifier using OpenCV to detect German road signs in images by implementing the Viola-Jones algorithm. It also provides details on the Viola-Jones algorithm, which combines Haar features, integral images, AdaBoost testing, and cascading classifiers to rapidly detect objects in real-time.
Leveraging Deep Learning Representation for search-based Image Annotation
The document presents a method for leveraging deep learning representations for search-based image annotation. The proposed method uses convolutional neural network (CNN) features extracted from pre-trained models to represent images. These features are then used for tag assignment through a nearest neighbor search. The method is evaluated on several datasets and achieves better performance than previous approaches, demonstrating the advantage of using rich CNN representations for image annotation. Experimental results show improved precision, recall, and F1 scores over existing methods.
Strategy for Foreground Movement Identification Adaptive to Background Variat...
Video processing has gained a lot of significance because of its applications in various areas of research. This includes monitoring movements in public places for surveillance. Video sequences from various standard datasets such as I2R, CAVIAR and UCSD are often referred for video processing applications and research. Identification of actors as well as the movements in video sequences should be accomplished with the static and dynamic background. The significance of research in video processing lies in identifying the foreground movement of actors and objects in video sequences. Foreground identification can be done with a static or dynamic background. This type of identification becomes complex while detecting the movements in video sequences with a dynamic background. For identification of foreground movement in video sequences with dynamic background, two algorithms are proposed in this article. The algorithms are termed as Frame Difference between Neighboring Frames using Hue, Saturation and Value (FDNF-HSV) and Frame Difference between Neighboring Frames using Greyscale (FDNF-G). With regard to F-measure, recall and precision, the proposed algorithms are evaluated with state-of-art techniques. Results of evaluation show that, the proposed algorithms have shown enhanced performance.
What's hot (20)
Modeling perceptual similarity and shift invariance in deep networks
Modeling perceptual similarity and shift invariance in deep networks
Face Detection System on Ada boost Algorithm Using Haar Classifiers
Face Detection System on Ada boost Algorithm Using Haar Classifiers
[CVPR2020] Simple but effective image enhancement techniques
[CVPR2020] Simple but effective image enhancement techniques
Color based image processing , tracking and automation using matlab
Color based image processing , tracking and automation using matlab
IRJET- Concepts, Methods and Applications of Neural Style Transfer: A Rev...
IRJET- Concepts, Methods and Applications of Neural Style Transfer: A Rev...
Review : Structure Boundary Preserving Segmentation
for Medical Image with Am...
Review : Structure Boundary Preserving Segmentation
for Medical Image with Am...
IRJET- Transformation of Realistic Images and Videos into Cartoon Images and ...
IRJET- Transformation of Realistic Images and Videos into Cartoon Images and ...
Image Maximization Using Multi Spectral Image Fusion Technique
Image Maximization Using Multi Spectral Image Fusion Technique
Road signs detection using voila jone's algorithm with the help of opencv
Road signs detection using voila jone's algorithm with the help of opencv
Leveraging Deep Learning Representation for search-based Image Annotation
Leveraging Deep Learning Representation for search-based Image Annotation
Strategy for Foreground Movement Identification Adaptive to Background Variat...
Strategy for Foreground Movement Identification Adaptive to Background Variat...
Similar to Unsupervised image to-image translation via pre-trained style gan2 network
"Enabling Ubiquitous Visual Intelligence Through Deep Learning," a Keynote Pr...
"Enabling Ubiquitous Visual Intelligence Through Deep Learning," a Keynote Pr...Edge AI and Vision Alliance
For the full video of this presentation, please visit:
http://www.embedded-vision.com/platinum-members/embedded-vision-alliance/embedded-vision-training/videos/pages/may-2015-embedded-vision-summit-baidu
For more information about embedded vision, please visit:
http://www.embedded-vision.com
Dr. Ren Wu, former distinguished scientist at Baidu's Institute of Deep Learning (IDL), presents the keynote talk, "Enabling Ubiquitous Visual Intelligence Through Deep Learning," at the May 2015 Embedded Vision Summit.
Deep learning techniques have been making headlines lately in computer vision research. Using techniques inspired by the human brain, deep learning employs massive replication of simple algorithms which learn to distinguish objects through training on vast numbers of examples. Neural networks trained in this way are gaining the ability to recognize objects as accurately as humans.
Some experts believe that deep learning will transform the field of vision, enabling the widespread deployment of visual intelligence in many types of systems and applications. But there are many practical problems to be solved before this goal can be reached. For example, how can we create the massive sets of real-world images required to train neural networks? And given their massive computational requirements, how can we deploy neural networks into applications like mobile and wearable devices with tight cost and power consumption constraints?
In this talk, Ren shares an insider’s perspective on these and other critical questions related to the practical use of neural networks for vision, based on the pioneering work being conducted by his former team at Baidu.
Note 1: Regarding the ImageNet results included in this presentation, the organizers of the ImageNet Large Scale Visual Recognition Challenge (ILSVRC) have said: “Because of the violation of the regulations of the test server, these results may not be directly comparable to results obtained and reported by other teams.” (http://www.image-net.org/challenges/LSVRC/announcement-June-2-2015)
Note 2: The presenter, Ren Wu, has told the Embedded Vision Alliance that “There was some ambiguity with the rules. According to the ‘official’ interpretation of the rules, there should be no more than 52 submissions within a half year. For us, we achieved the reported results after 200 tests total within a half year. We believe there is no way to obtain any measurable gains, nor did we try to obtain any gains, from an 'extra' hundred tests as our networks have billions of parameters and are trained by tens of billions of training samples.”Using Crowdsourced Images to Create Image Recognition Models with Analytics Z...
Volunteers around the world increasingly act as human sensors to collect millions of data points. A team from the World Bank trained deep learning models, using Apache Spark and BigDL, to confirm that photos gathered through a crowdsourced data collection pilot matched the goods for which observations were submitted.
In this talk, Maurice Nsabimana, a statistician at the World Bank, and Jiao Wang, a software engineer on the Big Data Technology team at Intel, demonstrate a collaborative project to design and train large-scale deep learning models using crowdsourced images from around the world. BigDL is a distributed deep learning library designed from the ground up to run natively on Apache Spark. It enables data engineers and scientists to write deep learning applications in Scala or Python as standard Spark programs-without having to explicitly manage distributed computations. Attendees of this session will learn how to get started with BigDL, which runs in any Apache Spark environment, whether on-premise or in the Cloud.
Smart environment for industry 4.0
This project contains an application to empower Industry 4.0 in Pakistan through Computer Vision techniques and approaches, a smart environment is to be created using different techniques shown in slides. Do get in touch for more details.
Latent diffusions vs DALL-E v2
Quick overview and comparison of the latest text-to-image models: Latent diffusions and DALL-E 2 (unCLIP).
How to use transfer learning to bootstrap image classification and question a...
1. The presentation discusses how to use transfer learning to bootstrap image classification and question answering tasks. Transfer learning allows leveraging knowledge from existing models trained on large datasets and applying it to new tasks with less data.
2. For image classification, the presentation recommends using features from pretrained convolutional neural networks on ImageNet as general purpose image features. Fine-tuning the top layers of these networks on smaller datasets can achieve good accuracy.
3. For natural language processing tasks, transfer learning techniques like using pretrained word embeddings, language models like ULMFiT and ELMo, and models trained on question answering datasets can help bootstrap tasks with less text data.
Deep Generative Modelling
This document provides an overview of deep generative models for images. It discusses generative adversarial networks (GANs) which define generative modeling as an adversarial game between a generator and discriminator. Conditional GANs can generate images from text or translate between image domains. Variational autoencoders (VAEs) learn latent representations of the data. Fully convolutional models use transposed convolutions in the decoder. CycleGAN can perform unpaired image-to-image translation using cycle consistency losses. Overall, generative models aim to understand data distributions in order to generate new, realistic samples.
HDF-EOS Java Application Programming Interfaces
HDF-EOS has been used extensively in the development of geospatial data web services and earth science data distribution systems in the CSISS center. Several popular open-source web application servers, e.g. Tomcat, are based on Java technology. Therefore, a suite of Java interfaces to call the HDF-EOS C library have been developed to facilitate the programming. JNI (Java Native Interface) is used to bridge the C library and the Java hierarchical wrap-up. In terms of implementation, all HDF-EOS 2.12 interfaces have been built for Java programming and these for HDF5-EOS are in the stage of development.
Next, objects, e.g. grid, field, band, are developed hierarchically based on these Java interfaces. Many conversion considerations to accommodate the different data types between C and Java are similar to those experienced for HDF Java product.
Review : Multi-Domain Image Completion for Random Missing Input Data [cdm]
Review : Multi-Domain Image Completion for Random Missing Input Data
Paper link : https://arxiv.org/abs/2007.05534
Computer vision-must-nit-silchar-ml-hackathon-2019
This document discusses various computer vision topics including convolutional neural networks (CNNs), popular CNN architectures, data augmentation, transfer learning, object detection, neural style transfer, generative adversarial networks (GANs), and variational autoencoders (VAEs). It provides overviews and explanations of each topic with examples. The goals are to introduce new concepts, discuss practical use cases, develop and improve intuitions, and provide tips for working on projects and participating in the community.
Image to image translation with Pix2Pix GAN
An Introduction to Pix2Pix GAN (Image-to-Image Translation with Conditional Adversarial Networks paper).
Efficient Image Processing - Nicolas Roard
The document discusses efficient image processing techniques for Android, focusing on the RenderScript framework. It provides an overview of RenderScript, how to write kernels in C, and how to call them from Java. Examples are given for common image processing tasks like grayscale conversion, bloom effects, and local adjustments. Caching strategies and handling low-memory devices are also covered to ensure performance across all hardware.
OReilly AI Transfer Learning
Transfer learning enables you to use pretrained deep neural networks trained on various large datasets (ImageNet, CIFAR, WikiQA, SQUAD, and more) and adapt them for various deep learning tasks (e.g., image classification, question answering, and more).
Wee Hyong Tok and Danielle Dean share the basics of transfer learning and demonstrate how to use the technique to bootstrap the building of custom image classifiers and custom question-answering (QA) models. You’ll learn how to use the pretrained CNNs available in various model libraries to custom build a convolution neural network for your use case. In addition, you’ll discover how to use transfer learning for question-answering tasks, with models trained on large QA datasets (WikiQA, SQUAD, and more), and adapt them for new question-answering tasks.
Topics include:
An introduction to convolution neural networks and question-answering problems
Using pretrained CNNs and the last fully connected layer as a featurizer (Once the features are extracted, any existing classifier can be used for image classification, using the extracted features as inputs.)
Fine-tuning the pretrained models and adapting them for the new images
Using pretrained QA models trained on large QA datasets (WikiQA, SQUAD) and applying transfer learning for QA tasks
Strata London - Deep Learning 05-2015
Deep learning techniques can be used to learn features from data rather than relying on hand-crafted features. This allows neural networks to be applied to problems in computer vision, natural language processing, and other domains. Transfer learning techniques take advantage of features learned from one task and apply them to another related task, even when limited data is available for the second task. Deploying machine learning models in production requires techniques for serving predictions through scalable APIs and caching layers to meet performance requirements.
Learn to Build an App to Find Similar Images using Deep Learning- Piotr Teterwak
This document discusses using deep learning and deep features to build an app that finds similar images. It begins with an overview of deep learning and how neural networks can learn complex patterns in data. The document then discusses how pre-trained neural networks can be used as feature extractors for other domains through transfer learning. This reduces data and tuning requirements compared to training new deep learning models. The rest of the document focuses on building an image similarity service using these techniques, including training a model with GraphLab Create and deploying it as a web service with Dato Predictive Services.
Let's paint a Picasso - A Look at Generative Adversarial Networks (GAN) and i...
This document provides an overview of Generative Adversarial Networks (GANs) and their applications. It explains the basic concepts of GANs including how they use generative and discriminative neural networks in an adversarial game-theory framework to generate new realistic data. Several types and applications of GANs are described, such as using GANs to generate images conditioned on text, edit images while preserving realism, and generate images of human poses. Challenges with GANs and potential future applications are also discussed.
How Will AI Change the Role of the Data Scientist?
Will AI make the sexiest job of the 21st century obsolete?
Talk from Helsinki Data Science meet-up at Sanoma on 2017-02-16.
Project_Final_Review.pdf
The document discusses the benefits of regular exercise for both physical and mental health. It notes that exercise can help reduce the risk of diseases like heart disease and diabetes, improve mood, and reduce feelings of stress and anxiety. Regular exercise of 150 minutes per week is recommended for substantial health benefits.
MongoDB in FS
MongoDB is a document-oriented, high performance, highly available, and horizontally scalable operational database. It addresses challenges with traditional RDBMS like handling high volumes of data, semi-structured and unstructured data types, and the need for agile development. MongoDB can be used for financial services use cases like high volume data feeds, risk analytics, product catalogs, trade capture, reporting, reference data management, portfolio management, quantitative analysis, and automated trading. It provides features like flexible schemas, indexing, aggregation, scaling out through sharding, and integration with Hadoop.
Fine tuning large LMs
This document discusses techniques for fine-tuning large pre-trained language models without access to a supercomputer. It describes the history of transformer models and how transfer learning works. It then outlines several techniques for reducing memory usage during fine-tuning, including reducing batch size, gradient accumulation, gradient checkpointing, mixed precision training, and distributed data parallelism approaches like ZeRO and pipelined parallelism. Resources for implementing these techniques are also provided.
DiscoGAN
This document summarizes the DiscoGAN model, which uses generative adversarial networks to discover relations between image domains without paired training examples. It introduces GANs and the DiscoGAN model, which uses two generators and discriminators with reconstruction and adversarial losses to learn bijective mappings between domains. Experiments show DiscoGAN can discover relations like azimuth angle between car images and translate attributes like gender between faces while maintaining other features. Code links for TensorFlow and PyTorch implementations are also provided.
Similar to Unsupervised image to-image translation via pre-trained style gan2 network (20)
"Enabling Ubiquitous Visual Intelligence Through Deep Learning," a Keynote Pr...
"Enabling Ubiquitous Visual Intelligence Through Deep Learning," a Keynote Pr...
Using Crowdsourced Images to Create Image Recognition Models with Analytics Z...
Using Crowdsourced Images to Create Image Recognition Models with Analytics Z...
How to use transfer learning to bootstrap image classification and question a...
How to use transfer learning to bootstrap image classification and question a...
Review : Multi-Domain Image Completion for Random Missing Input Data [cdm]
Review : Multi-Domain Image Completion for Random Missing Input Data [cdm]
Computer vision-must-nit-silchar-ml-hackathon-2019
Computer vision-must-nit-silchar-ml-hackathon-2019
Learn to Build an App to Find Similar Images using Deep Learning- Piotr Teterwak
Learn to Build an App to Find Similar Images using Deep Learning- Piotr Teterwak
Let's paint a Picasso - A Look at Generative Adversarial Networks (GAN) and i...
Let's paint a Picasso - A Look at Generative Adversarial Networks (GAN) and i...
How Will AI Change the Role of the Data Scientist?
How Will AI Change the Role of the Data Scientist?
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Open Source Contributions to Postgres: The Basics POSETTE 2024
Postgres is the most advanced open-source database in the world and it's supported by a community, not a single company. So how does this work? How does code actually get into Postgres? I recently had a patch submitted and committed and I want to share what I learned in that process. I’ll give you an overview of Postgres versions and how the underlying project codebase functions. I’ll also show you the process for submitting a patch and getting that tested and committed.
一比一原版莱斯大学毕业证（rice毕业证）如何办理
原版一模一样【微信：741003700 】【莱斯大学毕业证（rice毕业证）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理莱斯大学毕业证（rice毕业证）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理莱斯大学毕业证（rice毕业证）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
办理【微信号:176555708】【办理(UofT毕业证书)】【微信号:176555708】《成绩单、外壳、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号:176555708】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号:176555708】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
社内勉強会資料_Hallucination of LLMs .
社内エンジニア・リサーチャー勉強会の発表資料「Hallucination of LLMs」を公開しました！
大規模言語モデル(LLM)に起こるハルシネーションのメカニズムと、それらを軽減する方法について紹介しています。
一比一原版(uob毕业证书)伯明翰大学毕业证如何办理
原版一模一样【微信：6496090 】【(uob毕业证书)伯明翰大学毕业证成绩单】【微信：6496090 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微6496090
【主营项目】
一.毕业证【q微6496090】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微6496090】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理(uob毕业证书)伯明翰大学毕业证【微信：6496090 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理(uob毕业证书)伯明翰大学毕业证【微信：6496090 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理(uob毕业证书)伯明翰大学毕业证【微信：6496090 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理(uob毕业证书)伯明翰大学毕业证【微信：6496090 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
Generative Classifiers: Classifying with Bayesian decision theory, Bayes’ rule, Naïve Bayes classifier.
Discriminative Classifiers: Logistic Regression, Decision Trees: Training and Visualizing a Decision Tree, Making Predictions, Estimating Class Probabilities, The CART Training Algorithm, Attribute selection measures- Gini impurity; Entropy, Regularization Hyperparameters, Regression Trees, Linear Support vector machines.
一比一原版悉尼大学毕业证如何办理
原版一模一样【微信：741003700 】【悉尼大学毕业证成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理悉尼大学毕业证【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理悉尼大学毕业证【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理悉尼大学毕业证【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理悉尼大学毕业证【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
原版办理【微信号:BYZS866】【南十字星大学毕业证(SCU毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
一比一原版英国赫特福德大学毕业证（hertfordshire毕业证书）如何办理
原版一模一样【微信：741003700 】【英国赫特福德大学毕业证（hertfordshire毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理英国赫特福德大学毕业证（hertfordshire毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版马来西亚博特拉大学毕业证（upm毕业证）如何办理
原版一模一样【微信：741003700 】【马来西亚博特拉大学毕业证（upm毕业证）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理马来西亚博特拉大学毕业证（upm毕业证）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理马来西亚博特拉大学毕业证（upm毕业证）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理马来西亚博特拉大学毕业证（upm毕业证）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理马来西亚博特拉大学毕业证（upm毕业证）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版美国帕森斯设计学院毕业证（parsons毕业证书）如何办理
原版一模一样【微信：741003700 】【美国帕森斯设计学院毕业证（parsons毕业证书）成绩单】【微信：741003700 】学位证，留信认证（真实可查，永久存档）原件一模一样纸张工艺/offer、雅思、外壳等材料/诚信可靠,可直接看成品样本，帮您解决无法毕业带来的各种难题！外壳，原版制作，诚信可靠，可直接看成品样本。行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备。十五年致力于帮助留学生解决难题，包您满意。
本公司拥有海外各大学样板无数，能完美还原。
1:1完美还原海外各大学毕业材料上的工艺：水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠。文字图案浮雕、激光镭射、紫外荧光、温感、复印防伪等防伪工艺。材料咨询办理、认证咨询办理请加学历顾问Q/微741003700
【主营项目】
一.毕业证【q微741003700】成绩单、使馆认证、教育部认证、雅思托福成绩单、学生卡等！
二.真实使馆公证(即留学回国人员证明,不成功不收费)
三.真实教育部学历学位认证（教育部存档！教育部留服网站永久可查）
四.办理各国各大学文凭(一对一专业服务,可全程监控跟踪进度)
如果您处于以下几种情况：
◇在校期间，因各种原因未能顺利毕业……拿不到官方毕业证【q/微741003700】
◇面对父母的压力，希望尽快拿到；
◇不清楚认证流程以及材料该如何准备；
◇回国时间很长，忘记办理；
◇回国马上就要找工作，办给用人单位看；
◇企事业单位必须要求办理的
◇需要报考公务员、购买免税车、落转户口
◇申请留学生创业基金
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
办理美国帕森斯设计学院毕业证（parsons毕业证书）【微信：741003700 】外观非常简单，由纸质材料制成，上面印有校徽、校名、毕业生姓名、专业等信息。
办理美国帕森斯设计学院毕业证（parsons毕业证书）【微信：741003700 】格式相对统一，各专业都有相应的模板。通常包括以下部分：
校徽：象征着学校的荣誉和传承。
校名:学校英文全称
授予学位：本部分将注明获得的具体学位名称。
毕业生姓名：这是最重要的信息之一，标志着该证书是由特定人员获得的。
颁发日期：这是毕业正式生效的时间，也代表着毕业生学业的结束。
其他信息：根据不同的专业和学位，可能会有一些特定的信息或章节。
办理美国帕森斯设计学院毕业证（parsons毕业证书）【微信：741003700 】价值很高，需要妥善保管。一般来说，应放置在安全、干燥、防潮的地方，避免长时间暴露在阳光下。如需使用，最好使用复印件而不是原件，以免丢失。
综上所述，办理美国帕森斯设计学院毕业证（parsons毕业证书）【微信：741003700 】是证明身份和学历的高价值文件。外观简单庄重，格式统一，包括重要的个人信息和发布日期。对持有人来说，妥善保管是非常重要的。
一比一原版英属哥伦比亚大学毕业证(UBC毕业证书)学历如何办理
原版办【微信号:BYZS866】【英属哥伦比亚大学毕业证(UBC毕业证书)】【微信号:BYZS866】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信号BYZS866】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信号BYZS866】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Experts live - Improving user adoption with AI
Bekijk de slides van onze sessie Enhancing Modern Workplace Efficiency op Experts Live 2024.
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
We are pleased to share with you the latest VCOSA statistical report on the cotton and yarn industry for the month of March 2024.
Starting from January 2024, the full weekly and monthly reports will only be available for free to VCOSA members. To access the complete weekly report with figures, charts, and detailed analysis of the cotton fiber market in the past week, interested parties are kindly requested to contact VCOSA to subscribe to the newsletter.
Overview IFM June 2024 Consumer Confidence INDEX Report.pdf
Overview IFM June 2024 Consumer Confidence INDEX Report.pdf
原版一比一爱尔兰都柏林大学毕业证(UCD毕业证书)如何办理
原版制作【微信:41543339】【爱尔兰都柏林大学毕业证(UCD毕业证书)】【微信:41543339】《成绩单、外壳、雅思、offer、真实留信官方学历认证（永久存档/真实可查）》采用学校原版纸张、特殊工艺完全按照原版一比一制作（包括：隐形水印，阴影底纹，钢印LOGO烫金烫银，LOGO烫金烫银复合重叠，文字图案浮雕，激光镭射，紫外荧光，温感，复印防伪）行业标杆！精益求精，诚心合作，真诚制作！多年品质 ,按需精细制作，24小时接单,全套进口原装设备，十五年致力于帮助留学生解决难题，业务范围有加拿大、英国、澳洲、韩国、美国、新加坡，新西兰等学历材料，包您满意。
【我们承诺采用的是学校原版纸张（纸质、底色、纹路）我们拥有全套进口原装设备，特殊工艺都是采用不同机器制作，仿真度基本可以达到100%，所有工艺效果都可提前给客户展示，不满意可以根据客户要求进行调整，直到满意为止！】
【业务选择办理准则】
一、工作未确定，回国需先给父母、亲戚朋友看下文凭的情况，办理一份就读学校的毕业证【微信41543339】文凭即可
二、回国进私企、外企、自己做生意的情况，这些单位是不查询毕业证真伪的，而且国内没有渠道去查询国外文凭的真假，也不需要提供真实教育部认证。鉴于此，办理一份毕业证【微信41543339】即可
三、进国企，银行，事业单位，考公务员等等，这些单位是必需要提供真实教育部认证的，办理教育部认证所需资料众多且烦琐，所有材料您都必须提供原件，我们凭借丰富的经验，快捷的绿色通道帮您快速整合材料，让您少走弯路。
留信网认证的作用:
1:该专业认证可证明留学生真实身份
2:同时对留学生所学专业登记给予评定
3:国家专业人才认证中心颁发入库证书
4:这个认证书并且可以归档倒地方
5:凡事获得留信网入网的信息将会逐步更新到个人身份内，将在公安局网内查询个人身份证信息后，同步读取人才网入库信息
6:个人职称评审加20分
7:个人信誉贷款加10分
8:在国家人才网主办的国家网络招聘大会中纳入资料，供国家高端企业选择人才
留信网服务项目：
1、留学生专业人才库服务（留信分析）
2、国（境）学习人员提供就业推荐信服务
3、留学人员区块链存储服务
【关于价格问题（保证一手价格）】
我们所定的价格是非常合理的，而且我们现在做得单子大多数都是代理和回头客户介绍的所以一般现在有新的单子 我给客户的都是第一手的代理价格，因为我想坦诚对待大家 不想跟大家在价格方面浪费时间
对于老客户或者被老客户介绍过来的朋友，我们都会适当给一些优惠。
选择实体注册公司办理，更放心，更安全！我们的承诺：客户在留信官方认证查询网站查询到认证通过结果后付款，不成功不收费！
Drownings spike from May to August in children
Did you know that drowning is a leading cause of unintentional death among young children? According to recent data, children aged 1-4 years are at the highest risk. Let's raise awareness and take steps to prevent these tragic incidents. Supervision, barriers around pools, and learning CPR can make a difference. Stay safe this summer!
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[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
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Overview IFM June 2024 Consumer Confidence INDEX Report.pdf
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Unsupervised image to-image translation via pre-trained style gan2 network
- 1. Unsupervised Image-to-Image Translation via Pre-trained StyleGAN2 Network Kwang Hee Lee
- 2. Abstract • Traditional I2I translation ✓ Train data in two or more domains together ✓ Require lots of computation resources ✓ Lower quality and many artifacts ✓ Training process could be unstable when the data in different domains are not balanced ✓ Modal collapse is more likely to happen • Proposed a new I2I translation method ✓ Generates a new model in the target domain ✓ via a series of model transformations ✓ on a pretrained StyleGAN2 model in the source domain ✓ Proposed an inversion method
- 3. Related Works Image Translation Paired Unpaired Unimodal Multi-modal Unimodal Multi-modal Multi-domain Multi-mapping Pix2Pix Pix2PixHD BicycleGAN CycleGAN DiscoGAN UNIT MUNIT DRIT StarGAN DRIT++ DMIT SDIT StarGAN v2
- 5. Related Works-Multi-domain Translation StarGAN (CVPR2018) StarGAN: Unified Generative Adversarial Networks for Multi-Domain Image-to-Image Translation Yunjey Choi1,2, Minje Choi1,2, Munyoung Kim2,3, Jung-Woo Ha2, Sung Kim2,4, and Jaegul Choo1,2 IEEE CVPR, 2018 (Oral)
- 6. Related Works-Multi-modal Translation MUNIT (ECCV2018) DRIT (ECCV2018) Lee, Hsin-Ying, et al. "Diverse image-to-image translation via disentangled representations." Proceedings of the European Conference on Computer Vision (ECCV). 2018.
- 7. Related Works-Multi-mapping Translation Lee, Hsin-Ying, et al. "DRIT++: Diverse Image-to-Image Translation via Disentangled Representations." arXiv preprint arXiv:1905.01270 (2019). DRIT++(arXiv2019)
- 8. Related Works-Examplar Guided I2I Wang, Miao, et al. "Example-guided style-consistent image synthesis from semantic labeling." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019. Zhu, Zhen, et al. "Progressive pose attention transfer for person image generation." Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. 2019.
- 9. Related Works-Current I2I • Require online training on at least two domains • Domain-distinguished generators and discriminators • High demand on training resources in terms of both time and memory • Modal collapse when training data are not balanced in any domains
- 11. Related Works-GAN Inversion Mapping Zhu, Jiapeng, et al. "In-domain gan inversion for real image editing." European conference on computer vision. Springer, Cham, 2020. Goal: Image Editing Image2Image Translation
- 12. Major Contributions • Define the distance between two models to measure the semantic similarity between two images generated by two models, based on the same input latent vector • Propose an unsupervised I2I translation method via a pre-trained StyleGAN2 model • Support multi-modal and multi-domain I2I translation • Drastically improved the results and requires much less training resource • Proposed an inversion method is based on an embedded GAN space providing a boundary constraint for searching the latent code of the input image
- 13. Method • 가정: source domain 과 target domain의 모델간 semantic similarity가 작을 경우에 좋은 결과를 얻을 수 있음. • Worlflow • Pretrained StyleGAN2 model on source domain dataset Ds • Fine-tuning target domain model Gt using source domain model Gs and target dataset Dt • Source domain의 image가 주어지면, source domain model로 부터 latent code를 찾음. • 찾아진 latent code를 fine tuning된 target domain model에 넣어서 image를 생성함.
- 14. Method • Fine-tuning with data in target domain Dt • Freeze the FC layers in fine-tuning process • To have the same embedded space as the base model Fine tuning process Fine-tuned model
- 15. Method • During fine-tuning, the semantic similarity decreases due to domain difference. • Layer-swapping: source domain 의 더 많은 feature 들을 유지시키기 위해서 • Source domain model과 target domain model 사이의 model distance를 줄여주는 역할
- 18. Method From StyleGAN2 paper: Problem: w가 embedded space에 속하는지 보장하지 못함.
- 19. Method The difference between two latent codes: (n: a specific semantic attribute) (n에 대한 편미분)
- 20. Method
- 21. Method • Multi-modal and Multi-domain I2I translation • Style code injected at the higher layers(8x8, 16x16,…) can change a major structure of the output (identity, hair style, face shape) • Style code applied at lower layers can modify only minor features(color, light conditions and other micro-structures) • Multi-modal (다양한 스타일 생성) • Multi-domain (다양한 도메인 생성) 를 통해서 을 얻을 수 있음.
- 22. Experiment • Baseline models • CycleGAN • MUNIT • DRIT++ • Different scenarios • Face2portrait • Face2cartoon • Face2anime • Cat2dog • Cat2wild • FFHQ for face cases, AFHQ for cats, dogs and wild animals • Portrait (wikiart), anime(Danbooru2018) and cartoon(Toonify)
- 23. Expriment • Implementation Details • A2B • Freeze FC part of the A model • Fine-tuning: 12000(low resolution cases), 20000(high resolution cases) iterations • 1024 x 1024 (face2portrait, face2cartoon), 256 x 256 (face2anime) • 2080Ti GPU(11G), fine-tuning 2 days • Kept the training strategy and loss functions the same as those in the original StyleGAN2 • Layer-swap: 0.3ms, 8x8, 16x16, 32x32 , • Inversion process: 1000 iterations, 0.8s~1s
- 24. Experiment
- 26. Experiment-Reference guided image generation
- 27. Experiment-Ablation Study of Fine-tuning
- 28. Experiment • FT(Fine-tuning) • FC(Freeze FC layer) • LS(Layer Swap)
- 30. Experiment-Model Distance [55] Richard Zhang, Phillip Isola, Alexei A Efros, Eli Shechtman, and Oliver Wang. The unreasonable effectiveness of deep features as a perceptual metric. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pages 586–595, 2018.
- 31. Experiment
- 32. Experiment • 2000 test images chosen randomly • LPIPSd (Diversity): LPIPS between two randomly selected images in the generated results • LPIPSs (Semantic Similarity): LPIPS between the generated image and the input
- 34. Thank you.
- 35. StyleGAN