AI for Content Creation – CVPR2020 Virtual
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三次元点群を取り扱うニューラルネットワークのサーベイ Ver. 2 / Point Cloud Deep Learning Survey Ver. 2
興味を持った点群深層学習の関連の論文についてまとめました．図などは各論文から引用しています．（最近は論文が多く，あまり網羅はできていません） 間違いなどあればご指摘頂けるとありがたいです． 論文リスト: https://docs.google.com/spreadsheets/d/1l4NTSEpn6O1KbZO22CwUwojAyimGMSRABVDwV-wYK6Q/edit?usp=sharing Ver. 1: https://www.slideshare.net/naoyachiba18/ss-120302579
Introducing TensorFlow Graphics: Computer Graphics Meets Deep Learning
https://blog.tensorflow.org/2019/05/introducing-tensorflow-graphics_9.html https://blogger.googleusercontent.com/img/b/R29vZ2xl/AVvXsEiLtbFBddaXSQ9x-oBlhU_mUrk7nd9XpEr0bWXFRiE9lNjdeGKKf6Zl5DKbqn7eiaY032Wmck2JDliqalyJx_f43Y-4Mspjkz3X7eBze1o7NhSnsyDRvEIpHli9oqEFBuJshVeRt7cEmW02/s1600/tf+graphics.jpeg May 09, 2019 — Posted by Julien Valentin and Sofien Bouaziz Github repository: https://github.com/te
Semantic Image Synthesis with Spatially-Adaptive Normalization
Semantic Image Synthesis with Spatially-Adaptive Normalization We propose spatially-adaptive normalization, a simple but effective layer for synthesizing photorealistic images given an input semantic layout. Previous methods directly feed the semantic layout as input to the network, which is then processed through stacks of convolution, normalization, and nonlinearity layers. We show that this is
AIによるアニメ中割生成結果
DeNA「AIによるアニメ中割生成結果」：”ずんだホライずん*”でのテスト生成例** [技術詳細] “AIによるアニメ生成の挑戦”. 濱田晃一・李天琦 (DeNA TechCon 2019.) https://www.slideshare.net/hamadakoichi/anime-generation --- [Technical Details] “Challenges toward Anime Generation with Deep Generative Models”. Koichi Hamada and Tianqi Li. In DeNA Technology Conference 2019. https://www.slideshare.net/hamadakoichi/anime-generation --- *出典：SSS・STL・WAO 2017 「ず
AIによるアニメ生成の挑戦
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 metho
Papers with Code - Browse the State-of-the-Art in Machine Learning
Browse State-of-the-Art 10,955 benchmarks 4,908 tasks 131,075 papers with code
Generative Adversarial Networks (GANs) and Disentangled Representations
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 metho
NIPS 2018 Proceedings
Advances in Neural Information Processing Systems 31 (NIPS 2018) The papers below appear in Advances in Neural Information Processing Systems 31 edited by S. Bengio and H. Wallach and H. Larochelle and K. Grauman and N. Cesa-Bianchi and R. Garnett. They are proceedings from the conference, "Neural Information Processing Systems 2018." Efficient Algorithms for Non-convex Isotonic Regression through
深層強化学習アルゴリズムまとめ
はじめに 深層強化学習の分野では日進月歩で新たなアルゴリズムが提案されています. それらを学ぶ上で基礎となるアルゴリズム(というより概念に近い？)はQ学習, SARSA, 方策勾配法, Actor-Criticの4つだと思われるので, これらを軸としてまとめてみたいと思います. 以下の4点はあらかじめご了承ください. コードは書いていません. 概念のみの説明です 他のアルゴリズムの基礎となりうる重要な概念については詳しく書きました. その他については簡潔に書きました 深層学習についてはある程度理解している読者を想定しています 書いているうちに規模がどんどん大きくなってしまったので, どこかに必ず間違いや不足があります. 「この式がおかしい！」「このアルゴリズムも追加するべき！」などコメントがあればぜひお願いします 全体像 扱うアルゴリズムを相関図にしてみました(私のイメージです). まず,
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GitHub - google/style-aligned: Official code for "Style Aligned Image Generation via Shared Attention"
機械学習・AI活用した体験・サービス提供の挑戦10年
AI4CCW 2020
[CVPR 2020] Novel View Synthesis Tutorial
Novel View Synthesis: From Depth-Based Warping to Multi-Plane Images and Beyond
PyTorch Developer Conference | We are back! Tune in for the continuation of the PyTorch Developer Conference. We're picking up with Simon Wang from MIT in the PyTorch 1.3 Deep Dive... | By PyTorchFacebook
GitHub - moellenh/flatgan: PyTorch implementation of paper "Flat Metric Minimization with Applications in Generative Modeling"
AfterBurn | 株式会社Too
ECCV 2018 Open Access Repository