The document discusses recent advances in generative adversarial networks (GANs) for image generation. It summarizes two influential GAN models: ProgressiveGAN (Karras et al., 2018) and BigGAN (Brock et al., 2019). ProgressiveGAN introduced progressive growing of GANs to produce high resolution images. BigGAN scaled up GAN training through techniques like large batch sizes and regularization methods to generate high fidelity natural images. The document also discusses using GANs to generate full-body, high-resolution anime characters and adding motion through structure-conditional GANs.
The detailed results are described at GitHub (in English):
https://github.com/jkatsuta/exp-18-1q
(maddpg/experiments/my_notes/のexp1 ~ exp6)
立教大学のセミナー資料(前篇)です。
資料後篇:
https://www.slideshare.net/JunichiroKatsuta/ss-108099542
ブログ(動画あり):
https://recruit.gmo.jp/engineer/jisedai/blog/multi-agent-reinforcement-learning/
AAAI2023「Are Transformers Effective for Time Series Forecasting?」と、HuggingFace「Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)」の紹介です。
ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
Several recent papers have explored self-supervised learning methods for vision transformers (ViT). Key approaches include:
1. Masked prediction tasks that predict masked patches of the input image.
2. Contrastive learning using techniques like MoCo to learn representations by contrasting augmented views of the same image.
3. Self-distillation methods like DINO that distill a teacher ViT into a student ViT using different views of the same image.
4. Hybrid approaches that combine masked prediction with self-distillation, such as iBOT.
"Anime Generation with AI".
- Video: Generated Anime: https://youtu.be/X9j1fwexK2c
- Video: Other AI Solutions for Anime Production Issues: https://youtu.be/Gz90H1M7_u4
AAAI2023「Are Transformers Effective for Time Series Forecasting?」と、HuggingFace「Yes, Transformers are Effective for Time Series Forecasting (+ Autoformer)」の紹介です。
ゼロから始める深層強化学習(NLP2018講演資料)/ Introduction of Deep Reinforcement LearningPreferred Networks
Introduction of Deep Reinforcement Learning, which was presented at domestic NLP conference.
言語処理学会第24回年次大会(NLP2018) での講演資料です。
http://www.anlp.jp/nlp2018/#tutorial
Several recent papers have explored self-supervised learning methods for vision transformers (ViT). Key approaches include:
1. Masked prediction tasks that predict masked patches of the input image.
2. Contrastive learning using techniques like MoCo to learn representations by contrasting augmented views of the same image.
3. Self-distillation methods like DINO that distill a teacher ViT into a student ViT using different views of the same image.
4. Hybrid approaches that combine masked prediction with self-distillation, such as iBOT.
"Anime Generation with AI".
- Video: Generated Anime: https://youtu.be/X9j1fwexK2c
- Video: Other AI Solutions for Anime Production Issues: https://youtu.be/Gz90H1M7_u4
1) The document discusses the environmental impact of information and communication technologies (ICT) and strategies for more sustainable software design.
2) ICT currently has a large carbon footprint equivalent to 40 nuclear plants and is growing faster than other sectors. Manufacturing and disposing of devices accounts for most of the impact.
3) More efficient software design can help reduce this impact by using fewer system resources and extending the usable lifetime of devices. Approaches like responsive design, microservices, and streaming APIs can significantly improve efficiency.
Description of the impact of efficient coding on the planet. Using less ressource saves water, GHG and energy. Efficient UX also makes our world more sustainable. Linked to UNEP report on eWaste. Thanks @greenit.
20210809 story book_driven_new_system_development_nuxtjs虎の穴 開発室
The document discusses using Storybook to develop a new system. It begins with an introduction and agenda, then describes Storybook and how it can be used to develop UI components separately from pages. It explains how to install Storybook, including for Nuxt.js projects. Methods for Storybook-driven development are presented, such as creating screen designs and specifications in Markdown. Benefits include parallel documentation creation and API mocking. In summary, Storybook may help reduce overall development costs while improving component management.
The document discusses the evolution of artificial intelligence from expert systems using if-then rules to modern deep learning approaches. Early AI used expert systems that encoded human knowledge as rules, but these systems were limited, expensive and difficult to update. Machine learning improved on this by using algorithms to learn patterns in data and make predictions. However, features still needed to be designed by engineers. Deep learning advanced the field by using neural networks that can learn their own features from raw data through many layers, leading to superior performance compared to prior methods in tasks like image recognition. The document outlines several real-world applications of modern AI in areas such as computer vision, generative design, and autonomous systems.
Nathan Shedroff (Seed Vault Ltd): Blockchain & VR: Vision for an Open & Trust...AugmentedWorldExpo
A talk from the XR4Good Track at AWE USA 2018 - the World's #1 XR Conference & Expo in Santa Clara, California May 30- June 1, 2018.
Nathan Shedroff (Seed Vault Ltd): Blockchain & VR: Vision for an Open & Trusted Bot Economy
This talk proposes a method to govern an Open Source approach to building computer interfaces. Seed Vault developers can take the open bot framework and construct their own avatar or bot and offer it to the wider community. Others can license that bot for their own use and receive SEED tokens in exchange – utility tokens that power the Seed Vault micro-economy. Seed Vault intends to decentralize AI, putting bots in the hands of more developers, companies and ultimately individual users.
http://AugmentedWorldExpo.com
Attention Is All You Need.
With these simple words, the Deep Learning industry was forever changed. Transformers were initially introduced in the field of Natural Language Processing to enhance language translation, but they demonstrated astonishing results even outside language processing. In particular, they recently spread in the Computer Vision community, advancing the state-of-the-art on many vision tasks. But what are Transformers? What is the mechanism of self-attention, and do we really need it? How did they revolutionize Computer Vision? Will they ever replace convolutional neural networks?
These and many other questions will be answered during the talk.
In this tech talk, we will discuss:
- A piece of history: Why did we need a new architecture?
- What is self-attention, and where does this concept come from?
- The Transformer architecture and its mechanisms
- Vision Transformers: An Image is worth 16x16 words
- Video Understanding using Transformers: the space + time approach
- The scale and data problem: Is Attention what we really need?
- The future of Computer Vision through Transformers
Speaker: Davide Coccomini, Nicola Messina
Website: https://www.aicamp.ai/event/eventdetails/W2021101110
ThinnkWare is a venture that provides robotics education kits and training. It aims to facilitate experiential learning in science and technology. It is a pioneer in robotics education in India and has trained over 15,000 students. The company's Mechanzo robotics kits allow students to explore science concepts hands-on by designing and building robots. Students learn skills like problem solving, teamwork and gain interest in STEM careers through working with the kits.
iPhone X: Steve Job's iPhone and Advanced PackagingBill Cardoso
It’s been 10 years since Steve Jobs introduced the iPhone to the world. Much has happened since then. Over this past decade, the iPhone became a reference design, and the object of desire of a legion of fans who wait anxiously for every launch of the Cupertino company. Undoubtedly, the most advanced iPhone in the market today, the iPhone X is a technology marvel. The double stacked boards, dual battery, and a face recognition sensor bring the iPhone X to a whole different level.
In this presentation, we’ll explore these technological advances by a live teardown of the iPhone X. The teardown will be followed by a detailed coverage of the technical details of critical parts of the device. This live teardown will be accompanied by x-ray and CT images of the iPhone X, so the audience will get unprecedented insights on what makes this iPhone tick. More importantly, we will explore the assembly process utilized to put the iPhone X together. This presentation is targeted at a wide technical audience looking for a better understanding on how advance consumer electronics are designed and assembled.
Uses established clustering technologies for redundancy
Boosts availability and reliability of IT resources
Automatically transitions to standby instances when active resources become unavailable
Protects mission-critical software and reusable services from single points of failure
Can cover multiple geographical areas
Hosts redundant implementations of the same IT resource at each location
Relies on resource replication for monitoring defects and unavailability conditions
About:
A helium boosting and decanting system is typically used in various industrial applications, particularly in the production and handling of gases, including helium including leak test of reciprocating cylinder. Here’s a brief overview of its components and functions:
Components
1. Helium Storage Tanks: High-pressure tanks that store helium@ 150 bars.
2. Boosting Pumps: Designed to boost helium pressure up to 150 bar, ensuring efficient flow throughout the system.
3. Decanting Unit: Separates liquid helium from gas, facilitating decanting at pressures of up to 2 bars.
4. Pressure Regulators: Maintain and control the pressure of helium during transport.
5. Control Valves: automatic control valve is provided for the flow and direction of helium through the system.
6. Piping and Fittings: High-quality, corrosion-resistant materials for safe transport.
Functions
• Boosting Pressure: The system boosts helium pressure up to 150 bar for various applications.
• Decanting: Safely decants helium, separating liquid from gas at pressures of up to 2 bar.
• Safety Measures: Equipped with relief valves and emergency shut-off systems to handle high pressures safely.
• Monitoring and Control: Sensors and automated controls monitor pressure and flow rates.
Application:
• Cryogenics: Cooling superconducting magnets in MRI machines and particle accelerators.
• Welding: Used as a shielding gas in welding processes.
• Research: Crucial for various scientific applications, including laboratories and space exploration.
Key Features:
• Helium Storage & Boosting System
• Decanting System
• Pressure Regulation & Monitoring
• Valves & Flow Control
• Filtration & Safety Components
• Structural & Material Specifications
• Automation & Electrical Components
Mozambique, a country with vast natural resources and immense potential, nevertheless faces several economic challenges, including high unemployment, limited access to energy, and an unstable power supply. Underdeveloped infrastructure has slowed the growth of industry and hampered people’s entrepreneurial ambitions, leaving many regions in the dark—literally and figuratively.
https://www.rofinolicuco.net/blog/how-renewable-energy-can-help-mozambique-grow-its-economy
Algorithm design techniques include:
Brute Force
Greedy Algorithms
Divide-and-Conquer
Dynamic Programming
Reduction / Transform-and-Conquer
Backtracking and Branch-and-Bound
Randomization
Approximation
Recursive Approach
What is an algorithm?
An Algorithm is a procedure to solve a particular problem in a finite number of steps for a finite-sized input.
The algorithms can be classified in various ways. They are:
Implementation Method
Design Method
Design Approaches
Other Classifications
In this article, the different algorithms in each classification method are discussed.
The classification of algorithms is important for several reasons:
Organization: Algorithms can be very complex and by classifying them, it becomes easier to organize, understand, and compare different algorithms.
Problem Solving: Different problems require different algorithms, and by having a classification, it can help identify the best algorithm for a particular problem.
Performance Comparison: By classifying algorithms, it is possible to compare their performance in terms of time and space complexity, making it easier to choose the best algorithm for a particular use case.
Reusability: By classifying algorithms, it becomes easier to re-use existing algorithms for similar problems, thereby reducing development time and improving efficiency.
Research: Classifying algorithms is essential for research and development in computer science, as it helps to identify new algorithms and improve existing ones.
Overall, the classification of algorithms plays a crucial role in computer science and helps to improve the efficiency and effectiveness of solving problems.
Classification by Implementation Method: There are primarily three main categories into which an algorithm can be named in this type of classification. They are:
Recursion or Iteration: A recursive algorithm is an algorithm which calls itself again and again until a base condition is achieved whereas iterative algorithms use loops and/or data structures like stacks, queues to solve any problem. Every recursive solution can be implemented as an iterative solution and vice versa.
Example: The Tower of Hanoi is implemented in a recursive fashion while Stock Span problem is implemented iteratively.
Exact or Approximate: Algorithms that are capable of finding an optimal solution for any problem are known as the exact algorithm. For all those problems, where it is not possible to find the most optimized solution, an approximation algorithm is used. Approximate algorithms are the type of algorithms that find the result as an average outcome of sub outcomes to a problem.
Example: For NP-Hard Problems, approximation algorithms are used. Sorting algorithms are the exact algorithms.
Serial or Parallel or Distributed Algorithms: In serial algorithms, one instruction is executed at a time while parallel algorithms are those in which we divide the problem into subproblems and execute them on different processors.
TASK-DECOMPOSITION BASED ANOMALY DETECTION OF MASSIVE AND HIGH-VOLATILITY SES...samueljackson3773
The Science Information Network (SINET) is a Japanese academic backbone network for more than 800
universities and research institutions. The characteristic of SINET traffic is that it is enormous and highly
variable
Cloud Cost Optimization for GCP, AWS, Azurevinothsk19
Reduce Cloud Waste across AWS, GCP, Azure and Optimize Cloud Cost with a structured approach and improve your bottomline or profitability. Decide whether you want to outsource or manage it in house.
32. #denatechcon
Generative Adversarial Nets.
Ian J. Goodfellow, Jean Pouget-Abadie, Mehdi Mirza, Bing Xu, David Warde-
Farley, Sherjil Ozair, Aaron Courville, Yoshua Bengio.
arXiv:1406.2661. In NIPS 2014.
36. #denatechcon
Progressive Growing of GANs for Improved Quality, Stability, and Variation.
Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen. In ICLR 2018.
(1024X1024)
(256x256)
37. #denatechcon
.441 7 545 7 4 /
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen. In ICLR 2018.
38. #denatechcon
/ 5. 44 5
Progressive Growing of GANs for Improved Quality, Stability, and Variation
Tero Karras, Timo Aila, Samuli Laine, Jaakko Lehtinen. In ICLR 2018.
41. #denatechcon
+ Spectral Normalization on Generator
+ Self Attention
+ Two Time Scale Update Rule
(512x512)
+ Spectral Normalization on Discriminator
+ Projection Discriminator
SNGAN with Projection (Miyato+, ICLR’18)
SAGAN (Zhang+, 18)
BigGAN (Brock+, ICLR’19)
+ Large Batch Size (256→2048)
+ Large Channel (64→96)
+ Shared Embedding
+ Hierarchical Latent Space
+ Truncation Trick
+ Orthogonal Regularization
+ First Singular Value Clamp
+ Zero-centered Gradient Penalty
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2018.
43. #denatechcon
(512x512)
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
44. #denatechcon
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
(512x512)
45. #denatechcon
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
(512x512)
46. #denatechcon
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
(512x512)
47. #denatechcon
(512x512)
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
48. #denatechcon
(512x512)
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
49. #denatechcon
Large Scale GAN Training for High Fidelity Natural Image Synthesis.
Andrew Brock, Jeff Donahue, Karen Simonyan. arXiv:1809.11096. In ICLR 2019.
(512x512)
114. #denatechcon
8 1 1
1
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation.
Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz. In CVPR 2018.
115. #denatechcon
/30 480 6 2/81 4C
+ 60 2
• 8 ,
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation.
Huaizu Jiang, Deqing Sun, Varun Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz. In CVPR 2018.
https://youtu.be/MjViy6kyiqs
Research at NVIDIA: Transforming Standard Video Into Slow Motion with AI
117. #denatechcon
N I 7 B7 =: B = P
77 = 7: :=D
• /0 (+ /0 ,
Video Frame Synthesis using Deep Voxel Flow.
Ziwei Liu, Raymond A. Yeh, Xiaoou Tang, Yiming Liu, Aseem Agarwala. In ICCV 2017.
BB F=CBC 67 ?. / 3: 1 B
Video Frame Synthesis using Deep Voxel Flow
118. #denatechcon
D
F 6 + 23C
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• ( ,24 c SV P J ,24 cP
/ ++ C
• 1 76 , P J SV P
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Super SloMo(Adobe)
Super SloMo
Deep Voxel Flow
Video Frame Synthesis using Deep Voxel Flow. Ziwei Liu, Raymond A. Yeh, Xiaoou Tang, Yiming Liu, Aseem Agarwala. In ICCV 2017.
Super SloMo: High Quality Estimation of Multiple Intermediate Frames for Video Interpolation. Huaizu Jiang, Deqing Sun, Varun
Jampani, Ming-Hsuan Yang, Erik Learned-Miller, Jan Kautz. In CVPR 2018.
F