Idolmaster with DCGAN
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• 背景と目的
– 現在のキャラデザイン：デザイナー
• 人件費，完成までの時間 がかかる
→ 才能がいらない・時間がかからないキャラデザイン
• 手段と結果
– DCGAN(Deep ConvolutionalGAN) [Radford+, 2015]
によりキャラクターを自動生成
– それらしい画像の生成に成功
概要](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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Dennis Ritchie, a computer scientist, was able to identify the gaps and tap out the best features from both B and BCPL languages to invent a new hybrid.
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C++ is an enhanced and extended version of C programming language, developed by Bjarne Stroustrup in 1979 as part of his Ph.D. project. Bjarne developed what he called ‘C with Classes’ (later renamed C++) because he felt limited by the existing programming languages that were not ideal for large scale projects. He used C to build what he wanted because C was already a general-purpose language that was efficient and fast in its operations.
✅C++ Career Prospects:
With just C++ programming expertise, you will have excellent job opportunities, salaries, and career prospects. However, for a career based on programming languages such as Java and Python (which are in more demand than C++) or for careers based on front-end, back-end, and full-stack
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Has your project been caught in a storm of deadlines, clashing requirements, and the need to change course halfway through? If yes, then check out how the administration team navigated through all of this, relocating 160 people from 3 countries and opening 2 offices during the most turbulent time in the last 20 years. Belka Games’ Chief Administrative Officer, Katerina Rudko, will share universal approaches and life hacks that can help your project survive unstable periods when there seem to be too many tasks and a lack of time and people.
Barbie - Brand Strategy Presentation
This presentation was designed to provide strategic recommendations for a brand in decline. The deck also incorporates a situational assessment, including a brand identity, positioning, architecture, and portfolio strategy for the Brand.
Presentation originally created for NYU Stern's Brand Strategy course. Design by Erica Santiago & Chris Alexander.
Good Stuff Happens in 1:1 Meetings: Why you need them and how to do them well
According to the latest State of the American Manager report from Gallup, employees who have regular meetings with their managers are almost three times as likely to be engaged as those who don’t. These regular check-ins keep managers and employees in sync and aligned. Want to see better manager/employee relationships in your organisation? Then make an all-in commitment to 1:1 meetings. Not sure how? You’ve come to the right place.
In this webinar with Jamie Resker, Founder and Practice Leader for Employee Performance Solutions (EPS), and Teala Wilson, Talent Management Consultant at Saba Software, you’ll get the inside track on how to hold effective 1:1 meetings, including tips for getting managers on board.
• Go beyond discussing the status of everyday work to higher level topics, including recognition, performance, development, and career aspirations
• Learn how to decide meeting frequency, what to cover, as well as roles and responsibilities of the manager and employee
• Understand how managers can build trust and make it comfortable for employees to provide upward feedback
• Unite your organisation with a unified approach to 1:1 meetings
Join us for this 1-hour webinar to get practical tips for building better manager-employee relationships with intention and purpose.
About the Speakers
Jamie Resker - Founder and Practice Leader for Employee Performance Solutions (EPS)
Jamie Resker, Practice Leader and Founder of Employee Performance Solutions, is a recognized innovator in performance management. She is the originator of the-the Performance Continuum Feedback Method® and Conversations to Optimize Employee Performance training program; tools and training that reshape communications between managers and employees to drive and align performance. Jamie is on the faculty for the Northeast Human Resources Association, is a contributor to Halogen Software's Talent Space Blog, and is an editorial advisory board member for HR Examiner.
Teala Wilson - Senior Consultant, Strategic Services, Saba Software
Teala is a Talent Management Consultant at Halogen Software, now a part of Saba Software. She has worked with teams on a national and global level supporting human resources in areas such as performance management, recruitment, employee benefit programs, training and talent development, workforce planning and internal communications. Teala also has a personal passion for visual arts and design.
Want to learn more? Join us for an upcoming Product Tour!
http://bit.ly/2yitfqu
Introduction to C Programming Language
This video by Simplilearn will explain to you Introduction to C Programming Language. Introduction to C Programming Language Tutorial For Beginners will explain to you the C language's history, C's importance, its features, real-world applications, and some of its advantages and disadvantages.
00:00 Introduction to C
1:42-History of C language
Dennis Ritchie, a computer scientist, could identify the gaps and tap out the best features from both B and BCPL languages to invent a new hybrid.
Hence, C was born in 1972 at Bell Laboratories. A remarkably simple and highly readable programming language resulted in groundbreaking advancements in the IT industry.
2:48-Importance and unraveling the powerful capabilities of C,
The widespread use of C started to take over the IT industry. Unraveling the potential of C, the designers began to discover new possibilities that led them to focus on the big picture.
3:56-C's cutting-edge features
The designers at Bell Laboratories ensured that their programming language solved the issues with B and BCPL and the ones they had foreseen.
6:35-The popular real-world applications of C
-UNIX operating system
-google file system
-Mozilla
-Graphical user interface
8:30-The advantages and disadvantages of C
10:34-The popular IT companies and their domains that employ C
· MasterCard
· IBM
· Flipkart
· Dell
· Twitter
· GitHub and twitch
11:09-First c program.
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#IntroductiontoCProgrammingLanguage #CLanguage #CProgramming #CProgram #CProgrammingLanguage #LearnCProgramming #HowToCodeInCForBeginners #CTutorialForBeginners #LearnCProgramming #Simplilearn
Dennis Ritchie, a computer scientist, was able to identify the gaps and tap out the best features from both B and BCPL languages to invent a new hybrid.
Hence, C was born in 1972 at Bell Laboratories. A remarkably simple and highly readable programming language resulted in groundbreaking advancements in the IT industry.
✅What is C++ Programming?
C++ is an enhanced and extended version of C programming language, developed by Bjarne Stroustrup in 1979 as part of his Ph.D. project. Bjarne developed what he called ‘C with Classes’ (later renamed C++) because he felt limited by the existing programming languages that were not ideal for large scale projects. He used C to build what he wanted because C was already a general-purpose language that was efficient and fast in its operations.
✅C++ Career Prospects:
With just C++ programming expertise, you will have excellent job opportunities, salaries, and career prospects. However, for a career based on programming languages such as Java and Python (which are in more demand than C++) or for careers based on front-end, back-end, and full-stack
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Idolmaster with DCGAN
- 1. June 2 Yamamoto laboratory M1 SHIDOYusuke 1 Cinderella GAN
- 2. 2 • 背景と目的 – 現在のキャラデザイン：デザイナー • 人件費，完成までの時間 がかかる → 才能がいらない・時間がかからないキャラデザイン • 手段と結果 – DCGAN(Deep ConvolutionalGAN) [Radford+, 2015] によりキャラクターを自動生成 – それらしい画像の生成に成功 概要
- 3. 3 1. 現存するキャラクターのセットからそれらしい 画像を生成する 1. 入力: 1. ランダムノイズ 2. 現存のキャラクター画像 2. 出力: 1. それらしい画像 2. 生成したキャラクターを調節する 目標
- 4. 4 • 「アイドルマスターシンデレラガールズ」 に登場するアイドル203名 4500枚 × (回転・拡大縮小) = 27000枚 データセット
- 5. 5 GAN [Goodfellow+, 2014] • 概念図 教師データ ランダムノイズ • Generator 入力: ランダムノイズ 出力: 画像 • Discriminator 入力: 画像 出力: 画像がX,Zに よるものである確率 相互学習
- 6. 6 Generator Network Discriminator • 各層にbatch-normalization • average-pooling • Discriminatorにleaky-LeRU • 各層係数0.0001でL2正規化
- 7. 7 • Discriminatorの予測: – 本物 → 1 – 偽物 → 0 • DiscriminatorのLoss: −log 𝐷 𝑥 − log(1 − 𝐷(𝐺 𝑧 )) • GeneratorのLoss: − log(𝐺 𝑧 ) • Adam optimizer 学習率0.0002で学習 • バッチサイズ 128 学習
- 10. 10 • Epoch50 学習
- 11. 11 • Epoch50 学習
- 12. 12 • Epoch200 学習
- 16. 16 • 画像の特徴を作るノイズの特定 – 例: 金髪の画像を集めてノイズの平均を取る → 金髪成分 生成画像の調節
- 17. 17 • 画像の特徴を作るノイズの特定 – 例: 金髪の画像を集めてノイズの平均を取る → 金髪成分 – 金髪成分を足して正規化 生成画像の調節 元 → +金髪→ -金髪→
- 18. 18 • 画像の特徴を作るノイズの特定 – 例: 金髪の画像を集めてノイズの平均を取る → 金髪成分 – 金髪成分を足して正規化 • なぜ正規化するのか – ノイズの角から生成される画像は見た目が良くない • 良い画像2枚の内分は良い画像→中心に近いと◎ • 元の論文では引き算を挟んでいた 生成画像の調節
- 19. 19 1. 現存するキャラクターのセットからそれらしい 画像を生成する → それらしいものを大量に生成できるようになった 2. 生成したキャラクターを調節する → 人力で成分を特定すれば調節ができるようになった 結果