{"action":"created","issue":{"url":"https://api.github.com/repos/jonas-koehler/s2cnn/issues/28","repository_url":"https://api.github.com/repos/jonas-koehler/s2cnn","labels_url":"https://api.github.com/repos/jonas-koehler/s2cnn/issues/28/labels{/name}","comments_url":"https://api.github.com/repos/jonas-koehler/s2cnn/issues/28/comments","events_url":"https://api.github.com/repos/jonas-koehler/s2cnn/issues/28/events","html_url":"https://github.com/jonas-koehler/s2cnn/issues/28","id":367517312,"node_id":"MDU6SXNzdWUzNjc1MTczMTI=","number":28,"title":"S2CNN Computational Complexity","user":{"login":"yudawen","id":33175103,"node_id":"MDQ6VXNlcjMzMTc1MTAz","avatar_url":"https://avatars2.githubusercontent.com/u/33175103?v=4","gravatar_id":"","url":"https://api.github.com/users/yudawen","html_url":"https://github.com/yudawen","followers_url":"https://api.github.com/users/yudawen/followers","following_url":"https://api.github.com/users/yudawen/following{/other_user}","gists_url":"https://api.github.com/users/yudawen/gists{/gist_id}","starred_url":"https://api.github.com/users/yudawen/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/yudawen/subscriptions","organizations_url":"https://api.github.com/users/yudawen/orgs","repos_url":"https://api.github.com/users/yudawen/repos","events_url":"https://api.github.com/users/yudawen/events{/privacy}","received_events_url":"https://api.github.com/users/yudawen/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":5,"created_at":"2018-10-07T03:32:25Z","updated_at":"2018-10-18T09:34:01Z","closed_at":null,"author_association":"NONE","body":"The computational cost of spherical convolution is too great. Is there any measure that makes spherical convolution can handle large images?"},"comment":{"url":"https://api.github.com/repos/jonas-koehler/s2cnn/issues/comments/430942099","html_url":"https://github.com/jonas-koehler/s2cnn/issues/28#issuecomment-430942099","issue_url":"https://api.github.com/repos/jonas-koehler/s2cnn/issues/28","id":430942099,"node_id":"MDEyOklzc3VlQ29tbWVudDQzMDk0MjA5OQ==","user":{"login":"yudawen","id":33175103,"node_id":"MDQ6VXNlcjMzMTc1MTAz","avatar_url":"https://avatars2.githubusercontent.com/u/33175103?v=4","gravatar_id":"","url":"https://api.github.com/users/yudawen","html_url":"https://github.com/yudawen","followers_url":"https://api.github.com/users/yudawen/followers","following_url":"https://api.github.com/users/yudawen/following{/other_user}","gists_url":"https://api.github.com/users/yudawen/gists{/gist_id}","starred_url":"https://api.github.com/users/yudawen/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/yudawen/subscriptions","organizations_url":"https://api.github.com/users/yudawen/orgs","repos_url":"https://api.github.com/users/yudawen/repos","events_url":"https://api.github.com/users/yudawen/events{/privacy}","received_events_url":"https://api.github.com/users/yudawen/received_events","type":"User","site_admin":false},"created_at":"2018-10-18T09:34:01Z","updated_at":"2018-10-18T09:34:01Z","author_association":"NONE","body":"Is so3_integrate  like to weighted global average pooling，one equivariant way to keep equivariance? For SO3 signal ,i think max pooling is not equivariant. This can be achieved by using a convolution kernel with size of  n × n×lenght of the dimension of gamma , but I am worried that this is not an equivariant operation, is'it?"}}
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