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This document discusses unsupervised cross-domain image generation using a generative adversarial network (GAN). It proposes using an encoder to map images to a latent space and a generator to map the latent space to images. The generator is trained as an autoencoder to reconstruct input images, while a discriminator is trained to distinguish real from generated images. The model is tested on unsupervised translation between domains like celebrity faces from different datasets.
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(追記：2015/5/22 更新バージョンをアップロードしました。そちらをごらんください)
なにもアップロードしていないのにたくさんの方がフォローしてくださっているので、とりあえず、これを上げます。マインドマップのままですみません。そのうちスライドの形にまとめたいと思います。
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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Metameme20141222 2
(追記：2015/5/22 更新バージョンをアップロードしました。そちらをごらんください)
なにもアップロードしていないのにたくさんの方がフォローしてくださっているので、とりあえず、これを上げます。マインドマップのままですみません。そのうちスライドの形にまとめたいと思います。
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みなさんこんにちはこれ何文字まで入るの？40文字以下不可とか本当に意味わからないけどこれ限界文字数書いてないからマジでやばい文字数いけるんじゃないの？えこ...
ここ3000字までしか入らないけどタイトルの方がたくさん文字入ると思います。
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Welcome to UiPath Test Automation using UiPath Test Suite series part 6. In this session, we will cover Test Automation with generative AI and Open AI.
UiPath Test Automation with generative AI and Open AI webinar offers an in-depth exploration of leveraging cutting-edge technologies for test automation within the UiPath platform. Attendees will delve into the integration of generative AI, a test automation solution, with Open AI advanced natural language processing capabilities.
Throughout the session, participants will discover how this synergy empowers testers to automate repetitive tasks, enhance testing accuracy, and expedite the software testing life cycle. Topics covered include the seamless integration process, practical use cases, and the benefits of harnessing AI-driven automation for UiPath testing initiatives. By attending this webinar, testers, and automation professionals can gain valuable insights into harnessing the power of AI to optimize their test automation workflows within the UiPath ecosystem, ultimately driving efficiency and quality in software development processes.
What will you get from this session?
1. Insights into integrating generative AI.
2. Understanding how this integration enhances test automation within the UiPath platform
3. Practical demonstrations
4. Exploration of real-world use cases illustrating the benefits of AI-driven test automation for UiPath
Topics covered:
What is generative AI
Test Automation with generative AI and Open AI.
UiPath integration with generative AI
Speaker:
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
Infrastructure Challenges in Scaling RAG with Custom AI models
Building Retrieval-Augmented Generation (RAG) systems with open-source and custom AI models is a complex task. This talk explores the challenges in productionizing RAG systems, including retrieval performance, response synthesis, and evaluation. We’ll discuss how to leverage open-source models like text embeddings, language models, and custom fine-tuned models to enhance RAG performance. Additionally, we’ll cover how BentoML can help orchestrate and scale these AI components efficiently, ensuring seamless deployment and management of RAG systems in the cloud.
TrustArc Webinar - 2024 Global Privacy Survey
How does your privacy program stack up against your peers? What challenges are privacy teams tackling and prioritizing in 2024?
In the fifth annual Global Privacy Benchmarks Survey, we asked over 1,800 global privacy professionals and business executives to share their perspectives on the current state of privacy inside and outside of their organizations. This year’s report focused on emerging areas of importance for privacy and compliance professionals, including considerations and implications of Artificial Intelligence (AI) technologies, building brand trust, and different approaches for achieving higher privacy competence scores.
See how organizational priorities and strategic approaches to data security and privacy are evolving around the globe.
This webinar will review:
- The top 10 privacy insights from the fifth annual Global Privacy Benchmarks Survey
- The top challenges for privacy leaders, practitioners, and organizations in 2024
- Key themes to consider in developing and maintaining your privacy program
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写真から似顔絵
- 1. (Unsupervised Cross-Domain Image Generation) Takao Ikoma
- 2. ▸ : ? ▸ ▸ Facebook AI Research: ▸ ▸ Stanford Univ: ▸ ▸ : Pytorch Chainer
- 6. ▸ ▸ ms-celeb-1m( 180 )
- 7. Encoder ▸ Facebook Deepface ( ) ▸ SphereFace, Openface ▸ Sphereface ▸ 512 ▸ caffemodel ▸ caffemodel chainer ▸ encoder
- 8. ▸ Generator Loss Function ▸ Discriminator Loss Function ▸
- 9. ▸ ▸ ▸ ▸ ▸
- 11. ▸
- 12. ▸ ?????
- 13. ‣ Y. Taigman, A. Polyak, L. Wolf, “Unsupervised Cross-Domain Image Generation” ‣ arXiv preprint arXiv:1611.02200 (2016). ‣ Xinru Hua, Davis Rempe, and Haotian Zhang, “Unsupervised Cross-Domain Image Generation” ‣ http://cs229.stanford.edu/proj2017/final-reports/5241608.pdf ‣ Weiyang Liu, Yandong Wen, Zhiding Yu, Ming Li, Bhiksha Raj, Le Song, SphereFace: Deep Hypersphere Embedding for Face Recognition ‣ https://arxiv.org/abs/1704.08063 ‣ An example implementation of DCGAN (https://arxiv.org/abs/1511.06434) using trainer. ‣ https://github.com/chainer/chainer/tree/master/examples/dcgan