Disentanglement Survey:Can You Explain How Much Are Generative models Disentangled?
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Disentanglement Survey:Can You Explain How Much Are Generative models Disentangled?
Disentanglement Survey:Can You Explain How Much Are Generative models Disentangled?
Fashion is the way we present ourselves to the world and has become one of the world's largest industries. Fashion, mainly conveyed by vision, has thus attracted much attention from computer vision researchers in recent years. Given the rapid development, this paper provides a comprehensive survey of more than 200 major fashion-related works covering four main aspects for enabling intelligent fash
At Netflix, we want to entertain the world through creating engaging content and helping members discover the titles they will love. Key to that is understanding causal effects that connect changes we make in the product to indicators of member joy. To measure causal effects we rely heavily on AB testing, but we also leverage quasi-experimentation in cases where AB testing is limited. Many scienti
【日本のサウナ実態調査2023】/Japan Sauna Survey 2023 Sauna enthusiast population is on a rise.
【日本のサウナ実態調査2023】 3月7日は「サウナの日」(※1) サウナ愛好家人口、昨年の減少から復調の傾向に。サウナ利用者の4人に1人が個室型貸切サウナの利用経験あり ※1：「サウナの日」とは、公益社団法人 日本サウナ・スパ協会が申請し、一般社団法人 日本記念日協会に登録された。いわれは、サ（３）ウナ（７）の語呂合わせによるもの。日本各地でサウナシーンを盛り上げるイベントが開催される。 Revised to attach the English version of the report on March 14th, 2023 English follows Japanese 一般社団法人日本サウナ・温冷浴総合研究所は、2017年より続けている日本におけるサウナ・温冷浴の実態調査を行い、3/7のサウナの日に先立ってその調査結果を発表している。 これまでのリリース https://kyo
This document provides a survey of research on cross-modal embedding and retrieval between different media types such as text, images, video, and audio. It discusses several key areas including cross-modal retrieval which aims to retrieve relevant items across media types, audio-visual embedding to learn joint representations of audio and video, and applications such as sound localization, generat
GitHub - Yutong-Zhou-cv/Awesome-Text-to-Image: (ෆ`꒳´ෆ) A Survey on Text-to-Image Generation/Synthesis.
Text to Face👨🏻🧒👧🏼🧓🏽 (arXiv preprint 2024) [💬 3D] Portrait3D: Text-Guided High-Quality 3D Portrait Generation Using Pyramid Representation and GANs Prior, Yiqian Wu et al. [Paper] (CVPR 2024) CosmicMan: A Text-to-Image Foundation Model for Humans, Shikai Li et al. [Paper] [Project] (arXiv preprint 2024) Fast Text-to-3D-Aware Face Generation and Manipulation via Direct Cross-modal Mapping an
Robert Tjarko Lange Evolutionary Meta-Learning @sakana.ai @TU Berlin Metaphors are powerful tools to transfer ideas from one mind to another. Alan Kay introduced the alternative meaning of the term ‘desktop’ at Xerox PARC in 1970. Nowadays everyone - for a glimpse of a second - has to wonder what is actually meant when referring to a desktop. Recently, Deep Learning had the pleasure to welcome a n
【競馬 生放送】ベルモントステークス2020生放送 米国競馬 ライブ テレビ放送 生中継 実況中継 Survey
Question Title * 1. 【競馬 生放送】ベルモントステークス2020生放送 米国競馬 ライブ テレビ放送 生中継 実況中継 HD生放送リンクこちらへ https://bit.ly/2WYG9oq HD生放送リンクこちらへ https://bit.ly/2WYG9oq ベルモントステークス2020生放送 米国競馬 ライブ テレビ放送 生中継 実況中継 【米国競馬】ベルモントステークス チャット放送|ニコニコインフォ アメリカGI ベルモントステークスをみんなで見て、語るチャット生放送を放送いたします。 ... 米国競馬のクラシック3冠レース最終戦となるベルモントステークスは、 ... プリークネスステークス、ベルモントステークス）の中で最長距離であり、 ベルモントステークスとは (ベルモントステークスとは) [単語記事 ベルモントステー ベルモントステークスとは、アメリカ合衆
A Survey of FPGA Based Neural Network Accelerator 邦訳と感想 - Computer Science And Mathematics
今回は2/17日にドワンゴさんで行われたFPGAXでの発表で紹介したサーベイ論文「A Survey of FPGA Based Neural Network Accelerator」の邦訳を掲載することにする。 稚拙ながら発表スライドも以下にあるので興味のある方はぜひ御覧ください。( ´∀｀) https://www.slideshare.net/leapmind/an-introduction-of-dnn-compression-technology-and-hardware-acceleration-on-fpga-88557866 A Survey of FPGA Based Neural Network Accelerator Kaiyuan Guo, Shulin Zeng, Jincheng Yu, Yu Wang, Huazhong Yang https://arxiv.o
第二報 長期的な隆起を示す海成段丘と2024年能登半島地震の地殻変動｜災害と緊急調査｜産総研 地質調査総合センター / Geological Survey of Japan, AIST
活断層・火山研究部門 宍倉正展 能登半島には中期更新世（約77万年前）以降の海成段丘が発達しており、長期間にわたり地盤が隆起してきたことを示す。隆起はおもに断層活動によって地震時に生じると考えられ、2007年能登半島地震（M6.9）や2023年能登地方の地震（M6.5）に引き続き、今回の地震でも沿岸の隆起が測地観測データの解析によってすでに報告されている（国土地理院、2024）。 産総研地質調査総合センターでは、能登半島北部沿岸に分布する海成段丘のうち、特に完新世（最近約1万年）に形成されたと考えられる低位段丘や岩礁に固着した隆起生物遺骸群集（カンザシゴカイ類やフジツボ類）について、これまで10年以上に渡って調査を行ってきた。その一部は宍倉ほか（2020）によって報告している。低位段丘は基本的にL1〜L3面の3面に区分され、それらの高度分布を図1に示す。L1面の形成年代は今のところ不明であ
Python Developers Survey 2020 ResultsPython Developers Survey 2020 Results This is the fourth iteration of the official annual Python Developers Survey, conducted as a collaborative effort between the Python Software Foundation and JetBrains. In October 2020, more than 28,000 Python developers and enthusiasts from almost 200 countries/regions took the survey to reveal the current state of the lang
CONTENT MARKETING SURVEY2022 - 2023 | コンテンツマーケティング・サーベイ 2022 - 2023
調査概要 企画・実施 Content Marketing Academy 調査対象 コンテンツマーケティング・デイ2022の参加者、コンテンツマーケティングを実施している企業に所属している方、コンテンツマーケティングの戦略立案や、コンテンツマーケティング業務（コンテンツ制作含む）に関わる方。 調査方法 WEBアンケート調査 調査期間 2022年12月26日～2023年1月末日 利用について 本レポートの集計結果は下記にご留意の元、共有・転載が可能です。 特にContent Marketing Academyの事前承諾は不要です。 ※SNS(TWITTER / Facebook）での投稿の際のハッシュタグ： 「#CM_SURVEY」 引用元（出所）として、「Content Marketing Academy 実施 CONTENT MARKETING SURVEY 2022 - 2023」 を
You are here: Home > WebAIM Projects > Screen Reader User Survey #8 Results Introduction In August - September 2019, WebAIM surveyed preferences of screen reader users. We received 1224 valid responses. This was a follow-up to 7 previous surveys that were conducted between January 2009 and October 2017. A few disclaimers and notices: Totals may not equal 100% due to rounding. Total responses (n) f
Causal Machine Learning (CausalML) is an umbrella term for machine learning methods that formalize the data-generation process as a structural causal model (SCM). This perspective enables us to reason about the effects of changes to this process (interventions) and what would have happened in hindsight (counterfactuals). We categorize work in CausalML into five groups according to the problems the
In the January 2023 survey we received responses from 1,132,268,801 sites across 270,967,923 unique domains, and 12,156,700 web-facing computers. This reflects a gain of 6,894,269 sites, but a loss of 270,799 domains and 77,725 computers. Within the top million busiest sites, Cloudflare has jumped from 3rd to 1st place — overtaking both Apache and nginx in a single month — its market share increas
Text processing like humans do : visually attacking and shielding nlp systems[paper survey]
Text processing like humans do : visually attacking and shielding nlp systems[paper survey]
![Text processing like humans do : visually attacking and shielding nlp systems[paper survey]](https://cdn-ak-scissors.b.st-hatena.com/image/square/5f6bb8f5faa99be027d759cbd96bc7787172e642/height=288;version=1;width=512/https%3A%2F%2Fcdn.slidesharecdn.com%2Fss_thumbnails%2Ftextprocessinglikehumansdovisuallyattackingandshieldingnlpsystemspaper-survey-191102043730-thumbnail.jpg%3Fwidth%3D640%26height%3D640%26fit%3Dbounds)
In the past decade, we have witnessed the rise of deep learning to dominate the field of artificial intelligence. Advances in artificial neural networks alongside corresponding advances in hardware accelerators with large memory capacity, together with the availability of large datasets enabled practitioners to train and deploy sophisticated neural network models that achieve state-of-the-art perf
【オーストリアGP ライブ】 F1オーストリアGP 2020生放送 オーストリア グランプリ生放送 ライブ テレビ放送 生中継 実況中継 Survey
【オーストリアGP ライブ】 F1オーストリアGP 2020生放送 オーストリア グランプリ生放送 ライブ テレビ放送 生中継 実況中継 Question Title * 1. 【オーストリアGP ライブ】 F1オーストリアGP 2020生放送 オーストリア グランプリ生放送 ライブ テレビ放送 生中継 実況中継 HD生放送リンクこちらへ HD生放送リンクこちらへ 【オーストリアGP ライブ】 F1オーストリアGP 2020生放送 オーストリア グランプリ生放送 ライブ テレビ放送 生中継 実況中継 F1オーストリアGP 2020生放送 オーストリア グランプリ生放送 ライブ テレビ放送 生中継 実況中継 F1オーストリアGP 2020生放送 オーストリア グランプリ生放送 ライブ テレビ放送 生中継 実況中継F1オーストリアGP 2020生放送 オーストリア グランプリ生放送 ライブ テ
As usual, we conducted an annual community survey in 2021. We previously shared some some highlights and charts in a blog post. This year we would also like to make the complete (-ish) dataset available. We have compiled a report which contains data and charts for nearly all questions with minimal analysis. We have elided a few sensitive questions and have combined some answers or elided some answ
Graph: A Survey of Graph Neural Networks, Embedding, Tasks and Applications
グラフに関連する話題について幅広くサーベイを行い、30本の重要論文と70本の関連論文にまとめました。 ※こちらは発表のための縮小版です ※ 発表はこちらで行いました。動画もあります（ https://nlpaper-challenge.connpass.com/event/136090/ ） GNN, GCN, RelationalGCN and other GNNs, Link Prediction, Graph Classification, Graph Completion, Graph Representation/Embedding, Graph Kernel, Combinatorial/Logical, その他の最近の話題, CV, NLP, Molecular Graph Generation, Recommendation などの Application について、201
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Natural Language Generation (NLG) has improved exponentially in recent years thanks to the development of sequence-to-sequence deep learning technologies such as Transformer-based language models. This advancement has led to more fluent and coherent NLG, leading to improved development in downstream tasks such as abstractive summarization, dialogue generation and data-to-text generation. However,
In 2012, David Carr, a New York Times media columnist, spotted the way social media had begun to shift the balance of power in newsrooms.Credit...Paul Zimmerman/WireImage David Carr, the legendary Timesman who made this column a destination, told me back in 2012 that he kept a “helicopter on the roof” of The New York Times Building in case he needed to escape. After all, he had been taking shots a
Transformers have achieved great success in many artificial intelligence fields, such as natural language processing, computer vision, and audio processing. Therefore, it is natural to attract lots of interest from academic and industry researchers. Up to the present, a great variety of Transformer variants (a.k.a. X-formers) have been proposed, however, a systematic and comprehensive literature r
Survey of programming languages for sound and music
2021/03/19 SIGPX #8
Large Language Models (LLMs) have drawn a lot of attention due to their strong performance on a wide range of natural language tasks, since the release of ChatGPT in November 2022. LLMs' ability of general-purpose language understanding and generation is acquired by training billions of model's parameters on massive amounts of text data, as predicted by scaling laws \cite{kaplan2020scaling,hoffman
The 2022 State of Rust Survey is here! It's that time again! Time for us to take a look at who the Rust community is composed of, how the Rust project is doing, and how we can improve the Rust programming experience. The Rust Survey working group is pleased to announce our 2022 State of Rust Survey! Whether or not you use Rust today, we want to know your opinions. Your responses will help the proj
Hello, Rustaceans! The Rust Survey Team is excited to share the results of our 2023 survey on the Rust Programming language, conducted between December 18, 2023 and January 15, 2024. As in previous years, the 2023 State of Rust Survey was focused on gathering insights and feedback from Rust users, and all those who are interested in the future of Rust more generally. This eighth edition of the sur
Large language models (LLMs) have demonstrated remarkable capabilities across a broad spectrum of tasks. They have attracted significant attention and been deployed in numerous downstream applications. Nevertheless, akin to a double-edged sword, LLMs also present potential risks. They could suffer from private data leaks or yield inappropriate, harmful, or misleading content. Additionally, the rap
Human knowledge provides a formal understanding of the world. Knowledge graphs that represent structural relations between entities have become an increasingly popular research direction towards cognition and human-level intelligence. In this survey, we provide a comprehensive review of knowledge graph covering overall research topics about 1) knowledge graph representation learning, 2) knowledge
Global Design Survey Explore the data behind designer salaries, career trends, and what’s next for your design discipline. Introduction We kicked off the Dribbble Global Design Survey to better understand the design community and how and where it’s growing. We asked questions surrounding careers, salaries, skill building, remote work, and more—gleaning remarkable insights from designers worldwide.
なぜProduct Market Fit SurveyをするのかProduct Market Fit Surveyは、PMFの達成を判断する材料にするために行います。プロダクトをつくりはじめるとき、最初にめざす大きな目標がPMFです。PMFを達成することで、プロダクトに対する投資効率を最大化できます。 逆にいうと、PMFにいたっていない段階での投資は、穴の空いたバケツに水を注ぐようなものです。プロダクトはPMFを達成する必要があり、PMF Surveyはこの達成を判断するために重要な指標である、ということになります。 なぜProduct Market Fit Surveyなのかそもそも、なぜProduct Market Fit Surveyがよいのでしょうか。PMFを判断するためのテストは、PMF Survey以外にもNPS (Net Promoter Score)などがあります。 PMF
VIA Character Strengths Survey & Character Reports | VIA Institute
Discover Your Strengths Take the Free Strengths Survey to begin living your best life. Research shows that applying your strengths can increase confidence, happiness, positive relationships and reduces stress and anxiety. Discover your strengths today! Take The Survey Now! Help Others Build Their Strengths The VIA Survey is trusted by researchers and professionals around the world to assess charac
Greetings Rustaceans! Another year has passed, and with it comes another annual Rust survey analysis! The survey was conducted in the second half of September 2020 over a two-week period. We’d like to thank everyone who participated in this year’s survey with a special shout-out to those who helped translate non-English responses. Without further ado, let’s dive into the analysis! Survey Audience
In recent years, machine learning has transitioned from a field of academic research interest to a field capable of solving real-world business problems. However, the deployment of machine learning models in production systems can present a number of issues and concerns. This survey reviews published reports of deploying machine learning solutions in a variety of use cases, industries and applicat
111 A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT YIHAN CAO∗, Lehigh University & Carnegie Mellon University, USA SIYU LI, Lehigh University, USA YIXIN LIU, Lehigh University, USA ZHILING YAN, Lehigh University, USA YUTONG DAI, Lehigh University, USA PHILIP S. YU, University of Illinois at Chicago, USA LICHAO SUN, Lehigh University, USA Recen
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Fashion Meets Computer Vision: A Survey
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AIDB on X: "【GANの10年】今年はGANが誕生して10年目です。以下は、歴史的研究の抜粋です。 ○ Tanujit Chakraborty et al. Ten Years of Generative Adversarial Nets (GANs): A survey of the state-of-the-art ■2014年 GANが初めて誕生する ■2015年 高品質な画像を生成できるようになる（DCGAN）… https://t.co/7KbJWjR2Uz"
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マスク 安い 販売 || 安い使い捨てマスク Survey
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A Comprehensive Survey of AI-Generated Content (AIGC): A History of Generative AI from GAN to ChatGPT
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