{"version":"1.0","title":"Learning Spherical Convolution Using Graph Representation ","url":"https://iblog.ridge-i.com/entry/2021/04/14/110000","author_url":"https://blog.hatena.ne.jp/msabri/","image_url":"https://cdn-ak.f.st-hatena.com/images/fotolife/m/msabri/20210402/20210402173134.png","blog_url":"https://iblog.ridge-i.com/","blog_title":"Ridge-institute R&D Blog","provider_url":"https://hatena.blog","height":"190","published":"2021-04-14 11:00:00","description":"Hi! This is Ridge-i research and in today's article, Motaz Sabri will share with us some of our analysis and insights over Spherical Convolutions. When it comes to 2D plane image understanding, Convolutional Neural Networks (CNNs) will be the favorite choice for designing a learning model. However, \u2026","provider_name":"Hatena Blog","html":"<iframe src=\"https://hatenablog-parts.com/embed?url=https%3A%2F%2Fiblog.ridge-i.com%2Fentry%2F2021%2F04%2F14%2F110000\" title=\"Learning Spherical Convolution Using Graph Representation  - Ridge-institute R&amp;D Blog\" class=\"embed-card embed-blogcard\" scrolling=\"no\" frameborder=\"0\" style=\"display: block; width: 100%; height: 190px; max-width: 500px; margin: 10px 0px;\"></iframe>","author_name":"msabri","type":"rich","width":"100%","categories":["graph machine learning","spherical images"]}