Generative models (Geek hub 2021 lecture)

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Overview of generative models with the accent to the GANs and deep learning. Includes autoencoders, VAE, normalizing flows, autoregressive models, and a lot of GAN architectures.

Data & Analytics

Generative models
Vitaly Bondar
DL Enthusiast & Senior Researcher @ Neuromation
johngull @ gmail | ODS

Generative task
Discriminative task
P(X, Y) or P(Y)
P(Y | X)

Autoregressive models
Training Inference

Autoregressive models. PixelCNN++
Multiscale processing for long dependencies

Autoregressive models. PixelSnail
PixelCNN/PixelCNN++ PixelSnail

Autoregressive models
Pros:
● Likelihood models
● Generate nice details
● Generalization
Cons:
● Slow inference
● Tend to lost global features

Latent variable models (autoencoders)
Pros:
● Direct loss
● Simple latent recovery
Cons:
● Encoder output distribution is limited (diagonal-covariance gaussian)
● Blurred images

Normalizing ﬂows
f(x) - invertible and diﬀerentiable

Normalizing ﬂows. HINT (Hierarchical coupling ﬂow)

Normalizing ﬂows
Pros:
● Direct loss
● Simple latent recovery
● Exact distribution recovery
Cons:
● Lower quality than GANs for now
● Limitations for the model blocks

Generative adversarial networks
“I came up with this idea in a bar in Montreal, Canada
called "Three Winemakers." Ian Goodfellow

GAN. Critic penalties
WGAN-GP
R1/R2
Any auxiliary target!

GAN architectures: StyleGAN truncation trick

GAN. Data problem
● CelebA - 202,599
● CelebA-HQ - 300K
● FFHQ - 70K
● LSUN-Bedroom - 3,033,042
● LSUN-Church - 126,227
● LSUN-Car - 2,067,710

GAN. Data problem
What GAN will generate

GAN. Data problem
Zhao et al. Improved Consistency Regularization for GANs

GAN. Adaptive Discriminator Augmentation (StyleGAN2-ADA)

GAN. Adaptive Discriminator Augmentation

GANs: tricks
● ReLU -> LeakyRELU
● MaxPool -> AvgPool or Conv+stride
● Upsampling -> Deconvolution, PixelShuﬄe
● Use additional data if you have it
● Averaged generator
● Discriminator regularization
● Weights equalizing/special normalizations

이번 논문은 요즘 핫한 Diffusion을 처음으로 유행시킨 Denoising Diffusion Probabilistic Models (DDPM) 입니다. ICML 2015년에 처음 제안된 Diffusion의 여러 실용적인 측면들을 멋지게 해결하여 그 유행의 시작을 알린 논문인데요, Generative Model의 여러 분야와 Diffusion, 그리고 DDPM에서는 무엇이 바뀌었는지 알아보도록 하겠습니다. 논문 링크: https://arxiv.org/abs/2006.11239 영상 링크: https://youtu.be/1j0W_lu55nc

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This presentation explains basic ideas of graph neural networks (GNNs) and their common applications. Primary target audiences are students, engineers and researchers who are new to GNNs but interested in using GNNs for their projects. This is a modified version of the course material for a special lecture on Data Science at Nara Institute of Science and Technology (NAIST), given by Preferred Networks researcher Katsuhiko Ishiguro, PhD.

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GANs are a type of deep neural network that allow us to generate data. In this webinar, we’ll take a look at the concept and theory behind GANs, which can be used to train neural nets with data that is generated by the network. We’ll explore the GAN framework along with its components -- generator and discriminator networks. We’ll then learn how to use Apache MXNet on AWS using the popular MNIST dataset, which contains images of handwritten numbers. In the end, we’ll create a GAN model that is able to generate similar images of handwritten numbers from our test dataset.

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GANs are the new hottest topic in the ML arena; however, they present a challenge for the researchers and the engineers alike. Their design, and most importantly, the code implementation has been causing headaches to the ML practitioners, especially when moving to production. Starting from the very basic of what a GAN is, passing trough Tensorflow implementation, using the most cutting-edge APIs available in the framework, and finally, production-ready serving at scale using Google Cloud ML Engine. Slides for the talk: https://www.pycon.it/conference/talks/deep-diving-into-gans-form-theory-to-production Github repo: https://github.com/zurutech/gans-from-theory-to-production

Generative AI at the edge.pdf

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Generative AI models, such as ChatGPT and Stable Diffusion, can create new and original content like text, images, video, audio, or other data from simple prompts, as well as handle complex dialogs and reason about problems with or without images. These models are disrupting traditional technologies, from search and content creation to automation and problem solving, and are fundamentally shaping the future user interface to computing devices. Generative AI can apply broadly across industries, providing significant enhancements for utility, productivity, and entertainment. As generative AI adoption grows at record-setting speeds and computing demands increase, on-device and hybrid processing are more important than ever. Just like traditional computing evolved from mainframes to today’s mix of cloud and edge devices, AI processing will be distributed between them for AI to scale and reach its full potential. In this presentation you’ll learn about: - Why on-device AI is key - Full-stack AI optimizations to make on-device AI possible and efficient - Advanced techniques like quantization, distillation, and speculative decoding - How generative AI models can be run on device and examples of some running now - Qualcomm Technologies’ role in scaling on-device generative AI

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Slides by Víctor Garcia about: Phillip Isola, Jun-Yan Zhu, Tinghui Zhou, Alexei A. Efros, "Image-to-Image Translation Using Conditional Adversarial Networks". In arxiv, 2016. https://phillipi.github.io/pix2pix/ We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either. We investigate conditional adversarial networks as a general-purpose solution to image-to-image translation problems. These networks not only learn the mapping from input image to output image, but also learn a loss function to train this mapping. This makes it possible to apply the same generic approach to problems that traditionally would require very different loss formulations. We demonstrate that this approach is effective at synthesizing photos from label maps, reconstructing objects from edge maps, and colorizing images, among other tasks. As a community, we no longer hand-engineer our mapping functions, and this work suggests we can achieve reasonable results without hand-engineering our loss functions either.

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TWU毕业证【微信95270640】西三一大学没毕业>办理西三一大学毕业证成绩单【微信TWU】TWU毕业证成绩单TWU学历证书TWU文凭《TWU毕业套号文凭网认证西三一大学毕业证成绩单》《哪里买西三一大学毕业证文凭TWU成绩学校快递邮寄信封》《开版西三一大学文凭》TWU留信认证本科硕士学历认证 [留学文凭学历认证(留信认证使馆认证)西三一大学毕业证成绩单毕业证证书大学Offer请假条成绩单语言证书国际回国人员证明高仿教育部认证申请学校等一切高仿或者真实可查认证服务。多年留学服务公司,拥有海外样板无数能完美1:1还原海外各国大学degreeDiplomaTranscripts等毕业材料。海外大学毕业材料都有哪些工艺呢？工艺难度主要由：烫金.钢印.底纹.水印.防伪光标.热敏防伪等等组成。而且我们每天都在更新海外文凭的样板以求所有同学都能享受到完美的品质服务。国外毕业证学位证成绩单办理方法： 1客户提供办理西三一大学西三一大学本科学位证成绩单信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）； 2开始安排制作毕业证成绩单电子图； 3毕业证成绩单电子版做好以后发送给您确认； 4毕业证成绩单电子版您确认信息无误之后安排制作成品； 5成品做好拍照或者视频给您确认； 6快递给客户（国内顺丰国外DHLUPS等快读邮寄） — — — — 我们是挂科和未毕业同学们的福音我们是实体公司精益求精的工艺！ — — — - 一真实留信认证的作用(私企外企荣誉的见证): 1：该专业认证可证明留学生真实留学身份同时对留学生所学专业等级给予评定。 2：国家专业人才认证中心颁发入库证书这个入网证书并且可以归档到地方。 3：凡是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部网内查询个人身份证信息后同步读取人才网入库信息。 4：个人职称评审加20分个人信誉贷款加10分。 5：在国家人才网主办的全国网络招聘大会中纳入资料供国家500强等高端企业选择人才。广州火车东站的那一刻山娃感受到了一种从未有过的激动和震撼太美了可爱的广州父亲的城山娃惊喜得几乎叫出声来山娃觉得父亲太伟大了居然能单匹马地跑到这么远这么大这么美的地方赚大钱高楼大厦鳞次栉比大街小巷人潮涌动山娃一路张望一路惊叹他发现城里的桥居然层层叠叠扭来扭去桥下没水却有着水一般的车水马龙山娃惊诧于城里的公交车那么大那么美不用买票乖乖地掷下二枚硬币空调享受还能坐着看电视呢屡经辗转山娃终于跟着父亲到拉

Ch03-Managing the Object-Oriented Information Systems Project a.pdf

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一比一原版(UVic毕业证)维多利亚大学毕业证成绩单

ukgaet

UVic毕业证【微信95270640】（维多利亚大学毕业证成绩单本科学历）Q微信95270640(补办UVic学位文凭证书)维多利亚大学留信网学历认证怎么办理维多利亚大学毕业证成绩单精仿本科学位证书硕士文凭证书认证Seneca College diplomaoffer,Transcript办理硕士学位证书造假维多利亚大学假文凭学位证书制作UVic本科毕业证书硕士学位证书精仿维多利亚大学学历认证成绩单修改制作，办理真实认证、留信认证、使馆公证、购买成绩单，购买假文凭，购买假学位证，制造假国外大学文凭、毕业公证、毕业证明书、录取通知书、Offer、在读证明、雅思托福成绩单、假文凭、假毕业证、请假条、国际驾照、网上存档可查！【实体公司】办维多利亚大学维多利亚大学毕业证文凭证书学历认证学位证文凭认证办留信网认证办留服认证办教育部认证（网上可查实体公司专业可靠） — — — 留学归国服务中心 — — - 【主营项目】一.维多利亚大学毕业证成绩单使馆认证教育部认证成绩单等！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) 国外毕业证学位证成绩单办理流程： 1客户提供维多利亚大学维多利亚大学毕业证文凭证书办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询）； 2开始安排制作毕业证成绩单电子图； 3毕业证成绩单电子版做好以后发送给您确认； 4毕业证成绩单电子版您确认信息无误之后安排制作成品； 5成品做好拍照或者视频给您确认； 6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。专业服务请勿犹豫联系我！本公司是留学创业和海归创业者们的桥梁。一次办理终生受用一步到位高效服务。详情请在线咨询办理,欢迎有诚意办理的客户咨询!洽谈。招聘代理：本公司诚聘英国加拿大澳洲新西兰美国法国德国新加坡各地代理人员如果你有业余时间有兴趣就请联系我们咨询顾问：+微信:95270640刀劈开抑或用拳头砸开每人抱起一大块就啃啃得满嘴满脸猴屁股般的红艳大家一个劲地指着对方吃吃地笑瓜裂得古怪奇形怪状却丝毫不影响瓜味甜丝丝的满嘴生津遍地都是瓜横七竖八的活像掷满了一地的大石块摘走二三只爷爷是断然发现不了的即便发现爷爷也不恼反而教山娃辨认孰熟孰嫩孰甜孰淡名义上是护瓜往往在瓜棚里坐上一刻饱吃一顿后山娃就领着阿黑漫山遍野地跑阿黑是一条黑色的大猎狗挺机灵的是山娃多年的忠实伙伴平时山娃上学阿黑也静

一比一原版(CBU毕业证)卡普顿大学毕业证成绩单

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CBU毕业证【微信95270640】☀《卡普顿大学毕业证购买》GoogleQ微信95270640《CBU毕业证模板办理》加拿大文凭、本科、硕士、研究生学历都可以做,二、业务范围： ★、全套服务：毕业证、成绩单、化学专业毕业证书伪造《卡普顿大学大学毕业证》Q微信95270640《CBU学位证书购买》专业为留学生办理卡普顿大学卡普顿大学本科毕业证成绩单【100%存档可查】留学全套申请材料办理。本公司承诺所有毕业证成绩单成品全部按照学校原版工艺对照一比一制作和学校一样的羊皮纸张保证您证书的质量！如果你回国在学历认证方面有以下难题请联系我们我们将竭诚为你解决认证瓶颈 1所有材料真实但资料不全无法提供完全齐整的原件。【如：成绩单丶毕业证丶回国证明等材料中有遗失的。】 2获得真实的国外最终学历学位但国外本科学历就读经历存在问题或缺陷。【如：国外本科是教育部不承认的或者是联合办学项目教育部没有备案的或者外本科没有正常毕业的。】 3学分转移联合办学等情况复杂不知道怎么整理材料的。时间紧迫自己不清楚递交流程的。如果你是以上情况之一请联系我们我们将在第一时间内给你免费咨询相关信息。我们将帮助你整理认证所需的各种材料.帮你解决国外学历认证难题。国外卡普顿大学卡普顿大学本科毕业证成绩单办理方法： 1客户提供办理信息：姓名生日专业学位毕业时间等（如信息不确定可以咨询顾问：我们有专业老师帮你查询卡普顿大学卡普顿大学本科毕业证成绩单）； 2开始安排制作卡普顿大学毕业证成绩单电子图； 3卡普顿大学毕业证成绩单电子版做好以后发送给您确认； 4卡普顿大学毕业证成绩单电子版您确认信息无误之后安排制作成品； 5卡普顿大学成品做好拍照或者视频给您确认； 6快递给客户（国内顺丰国外DHLUPS等快读邮寄）。父亲太伟大了居然能单匹马地跑到这么远这么大这么美的地方赚大钱高楼大厦鳞次栉比大街小巷人潮涌动山娃一路张望一路惊叹他发现城里的桥居然层层叠叠扭来扭去桥下没水却有着水一般的车水马龙山娃惊诧于城里的公交车那么大那么美不用买票乖乖地掷下二枚硬币空调享受还能坐着看电视呢屡经辗转山娃终于跟着父亲到家了山娃没想到父亲城里的家会如此寒碜更没料到父亲的城里竟有如此简陋的鬼地方父亲的家在高楼最底屋最下面很矮很黑是子

一比一原版(UniSA毕业证书)南澳大学毕业证如何办理

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原版定制【微信:41543339】【(UniSA毕业证书)南澳大学毕业证】【微信:41543339】成绩单、外壳、offer、留信学历认证（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！

Empowering Data Analytics Ecosystem.pptx

benishzehra469

Show drafts volume_up Empowering the Data Analytics Ecosystem: A Laser Focus on Value The data analytics ecosystem thrives when every component functions at its peak, unlocking the true potential of data. Here's a laser focus on key areas for an empowered ecosystem: 1. Democratize Access, Not Data: Granular Access Controls: Provide users with self-service tools tailored to their specific needs, preventing data overload and misuse. Data Catalogs: Implement robust data catalogs for easy discovery and understanding of available data sources. 2. Foster Collaboration with Clear Roles: Data Mesh Architecture: Break down data silos by creating a distributed data ownership model with clear ownership and responsibilities. Collaborative Workspaces: Utilize interactive platforms where data scientists, analysts, and domain experts can work seamlessly together. 3. Leverage Advanced Analytics Strategically: AI-powered Automation: Automate repetitive tasks like data cleaning and feature engineering, freeing up data talent for higher-level analysis. Right-Tool Selection: Strategically choose the most effective advanced analytics techniques (e.g., AI, ML) based on specific business problems. 4. Prioritize Data Quality with Automation: Automated Data Validation: Implement automated data quality checks to identify and rectify errors at the source, minimizing downstream issues. Data Lineage Tracking: Track the flow of data throughout the ecosystem, ensuring transparency and facilitating root cause analysis for errors. 5. Cultivate a Data-Driven Mindset: Metrics-Driven Performance Management: Align KPIs and performance metrics with data-driven insights to ensure actionable decision making. Data Storytelling Workshops: Equip stakeholders with the skills to translate complex data findings into compelling narratives that drive action. Benefits of a Precise Ecosystem: Sharpened Focus: Precise access and clear roles ensure everyone works with the most relevant data, maximizing efficiency. Actionable Insights: Strategic analytics and automated quality checks lead to more reliable and actionable data insights. Continuous Improvement: Data-driven performance management fosters a culture of learning and continuous improvement. Sustainable Growth: Empowered by data, organizations can make informed decisions to drive sustainable growth and innovation. By focusing on these precise actions, organizations can create an empowered data analytics ecosystem that delivers real value by driving data-driven decisions and maximizing the return on their data investment.

SOCRadar Germany 2024 Threat Landscape Report

SOCRadar

As Europe's leading economic powerhouse and the fourth-largest hashtag#economy globally, Germany stands at the forefront of innovation and industrial might. Renowned for its precision engineering and high-tech sectors, Germany's economic structure is heavily supported by a robust service industry, accounting for approximately 68% of its GDP. This economic clout and strategic geopolitical stance position Germany as a focal point in the global cyber threat landscape. In the face of escalating global tensions, particularly those emanating from geopolitical disputes with nations like hashtag#Russia and hashtag#China, hashtag#Germany has witnessed a significant uptick in targeted cyber operations. Our analysis indicates a marked increase in hashtag#cyberattack sophistication aimed at critical infrastructure and key industrial sectors. These attacks range from ransomware campaigns to hashtag#AdvancedPersistentThreats (hashtag#APTs), threatening national security and business integrity. 🔑 Key findings include: 🔍 Increased frequency and complexity of cyber threats. 🔍 Escalation of state-sponsored and criminally motivated cyber operations. 🔍 Active dark web exchanges of malicious tools and tactics. Our comprehensive report delves into these challenges, using a blend of open-source and proprietary data collection techniques. By monitoring activity on critical networks and analyzing attack patterns, our team provides a detailed overview of the threats facing German entities. This report aims to equip stakeholders across public and private sectors with the knowledge to enhance their defensive strategies, reduce exposure to cyber risks, and reinforce Germany's resilience against cyber threats.

一比一原版(ArtEZ毕业证)ArtEZ艺术学院毕业证成绩单

vcaxypu

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Chatty Kathy - UNC Bootcamp Final Project Presentation - Final Version - 5.23...

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SlideShare Description for "Chatty Kathy - UNC Bootcamp Final Project Presentation" Title: Chatty Kathy: Enhancing Physical Activity Among Older Adults Description: Discover how Chatty Kathy, an innovative project developed at the UNC Bootcamp, aims to tackle the challenge of low physical activity among older adults. Our AI-driven solution uses peer interaction to boost and sustain exercise levels, significantly improving health outcomes. This presentation covers our problem statement, the rationale behind Chatty Kathy, synthetic data and persona creation, model performance metrics, a visual demonstration of the project, and potential future developments. Join us for an insightful Q&A session to explore the potential of this groundbreaking project. Project Team: Jay Requarth, Jana Avery, John Andrews, Dr. Dick Davis II, Nee Buntoum, Nam Yeongjin & Mat Nicholas

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Q1’2024 Update: MYCI’s Leap Year Rebound

Oppotus

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Criminal IP

The affect of service quality and online reviews on customer loyalty in the E...

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原版定制【Q微信:741003700】《(mqu毕业证书)麦考瑞大学毕业证硕士文凭证书》【Q微信:741003700】成绩单、雅思、外壳、留信学历认证永久存档查询，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。【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【Q微信741003700】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741003700】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【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。

Data Centers - Striving Within A Narrow Range - Research Report - MCG - May 2...

pchutichetpong

M Capital Group (“MCG”) expects to see demand and the changing evolution of supply, facilitated through institutional investment rotation out of offices and into work from home (“WFH”), while the ever-expanding need for data storage as global internet usage expands, with experts predicting 5.3 billion users by 2023. These market factors will be underpinned by technological changes, such as progressing cloud services and edge sites, allowing the industry to see strong expected annual growth of 13% over the next 4 years. Whilst competitive headwinds remain, represented through the recent second bankruptcy filing of Sungard, which blames “COVID-19 and other macroeconomic trends including delayed customer spending decisions, insourcing and reductions in IT spending, energy inflation and reduction in demand for certain services”, the industry has seen key adjustments, where MCG believes that engineering cost management and technological innovation will be paramount to success. MCG reports that the more favorable market conditions expected over the next few years, helped by the winding down of pandemic restrictions and a hybrid working environment will be driving market momentum forward. The continuous injection of capital by alternative investment firms, as well as the growing infrastructural investment from cloud service providers and social media companies, whose revenues are expected to grow over 3.6x larger by value in 2026, will likely help propel center provision and innovation. These factors paint a promising picture for the industry players that offset rising input costs and adapt to new technologies. According to M Capital Group: “Specifically, the long-term cost-saving opportunities available from the rise of remote managing will likely aid value growth for the industry. Through margin optimization and further availability of capital for reinvestment, strong players will maintain their competitive foothold, while weaker players exit the market to balance supply and demand.”

一比一原版(CBU毕业证)卡普顿大学毕业证如何办理

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一比一原版(UPenn毕业证)宾夕法尼亚大学毕业证成绩单

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Generative models (Geek hub 2021 lecture)

1. Generative models Vitaly Bondar DL Enthusiast & Senior Researcher @ Neuromation johngull @ gmail | ODS

2. Generative task Discriminative task P(X, Y) or P(Y) P(Y | X)

3. Solve as discriminative task

4. Solve as discriminative task

5. Autoregressive models

6. Autoregressive models Training Inference

7. Autoregressive models

8. Autoregressive models

9. Autoregressive models

10. Autoregressive models. PixelCNN++ Multiscale processing for long dependencies

11. Autoregressive models. PixelSnail PixelCNN/PixelCNN++ PixelSnail

12. Autoregressive models. PixelSnail

13. Autoregressive models. PixelSnail

14. Autoregressive models. Fast PixelCNN

15. Autoregressive models Pros: ● Likelihood models ● Generate nice details ● Generalization Cons: ● Slow inference ● Tend to lost global features

16. Autoencoder

17. Variational autoencoder (VAE)

18. Variational autoencoder (VAE)

19. Variational autoencoder (VAE)

20. Beta-VAE

21. Beta-VAE

22. VQ-VAE/VQ-VAE2

23. VQ-VAE2

24. NVAE

25. NVAE

26. NVAE

27. Latent variable models (autoencoders) Pros: ● Direct loss ● Simple latent recovery Cons: ● Encoder output distribution is limited (diagonal-covariance gaussian) ● Blurred images

28. Normalizing ﬂows

29. Normalizing ﬂows

30. Normalizing ﬂows f(x) - invertible and diﬀerentiable

36. Normalizing ﬂows. HINT (Hierarchical coupling ﬂow)

37. Normalizing ﬂows. Glow

38. Normalizing Flows. Glow

39. Normalizing Flows. SRFlow

40. Normalizing Flows. SRFlow

41. Normalizing ﬂows Pros: ● Direct loss ● Simple latent recovery ● Exact distribution recovery Cons: ● Lower quality than GANs for now ● Limitations for the model blocks

42. Generative adversarial networks “I came up with this idea in a bar in Montreal, Canada called "Three Winemakers." Ian Goodfellow

43. Generative adversarial networks

44. Generative adversarial networks

45. GAN. WGAN

46. GAN. Relativistic losses

47. GAN. Relativistic losses

48. GAN. Critic penalties WGAN-GP R1/R2 Any auxiliary target!

49. GAN architectures: DCGAN

50. GAN architectures: pix2pix (vid2vid)

51. GAN architectures: CycleGAN

52. GAN architectures: TransGAGA

53. GAN architectures: StarGAN

54. GAN architectures: AEGAN

55. GAN architectures: BicycleGAN

56. GAN architectures: BicycleGAN

57. GAN architectures: Self-supervised GAN

58. GAN architectures: Self-attention GAN

59. GAN architectures: ESRGAN

60. GAN architectures: Real-SR

61. GAN architectures: Text-to-Image

62. GAN architectures: BigGAN

63. GAN architectures: BigGAN (fun) results

64. GAN architectures: Progressive GAN

65. GAN architectures: StyleGAN

66. GAN architectures: StyleGAN noise eﬀect

67. GAN architectures: StyleGAN truncation trick

68. GAN architectures: MSG-GAN

69. GAN architectures: StyleGAN2

70.

71. GAN architectures: SinGan

72. GAN architectures: SinGan

73. GAN architectures: COCO-GAN

74. GAN architectures: GauGAN

75. GAN architectures: GauGAN

76. GAN architectures: inﬁnite nature

77. GAN architectures: inﬁnite nature

78. GAN architectures: FUNIT

79. GAN architectures: FUNIT

80. GAN architectures: Everybody dance now

81. GAN architectures: Everybody dance now

82. GAN. Data problem ● CelebA - 202,599 ● CelebA-HQ - 300K ● FFHQ - 70K ● LSUN-Bedroom - 3,033,042 ● LSUN-Church - 126,227 ● LSUN-Car - 2,067,710

83. GAN. Data problem What we usually do

84. GAN. Data problem What GAN will generate

85. GAN. Data problem Zhao et al. Improved Consistency Regularization for GANs

86. GAN. Adaptive Discriminator Augmentation (StyleGAN2-ADA)

87. GAN. Adaptive Discriminator Augmentation

88. GANs: tricks ● ReLU -> LeakyRELU ● MaxPool -> AvgPool or Conv+stride ● Upsampling -> Deconvolution, PixelShuﬄe ● Use additional data if you have it ● Averaged generator ● Discriminator regularization ● Weights equalizing/special normalizations

89. Thank you.

Generative models (Geek hub 2021 lecture)

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