{"action":"created","issue":{"url":"https://api.github.com/repos/GuoShi28/CBDNet/issues/7","repository_url":"https://api.github.com/repos/GuoShi28/CBDNet","labels_url":"https://api.github.com/repos/GuoShi28/CBDNet/issues/7/labels{/name}","comments_url":"https://api.github.com/repos/GuoShi28/CBDNet/issues/7/comments","events_url":"https://api.github.com/repos/GuoShi28/CBDNet/issues/7/events","html_url":"https://github.com/GuoShi28/CBDNet/issues/7","id":374268499,"node_id":"MDU6SXNzdWUzNzQyNjg0OTk=","number":7,"title":"A question about the loss","user":{"login":"XSLXANDY","id":29296287,"node_id":"MDQ6VXNlcjI5Mjk2Mjg3","avatar_url":"https://avatars3.githubusercontent.com/u/29296287?v=4","gravatar_id":"","url":"https://api.github.com/users/XSLXANDY","html_url":"https://github.com/XSLXANDY","followers_url":"https://api.github.com/users/XSLXANDY/followers","following_url":"https://api.github.com/users/XSLXANDY/following{/other_user}","gists_url":"https://api.github.com/users/XSLXANDY/gists{/gist_id}","starred_url":"https://api.github.com/users/XSLXANDY/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/XSLXANDY/subscriptions","organizations_url":"https://api.github.com/users/XSLXANDY/orgs","repos_url":"https://api.github.com/users/XSLXANDY/repos","events_url":"https://api.github.com/users/XSLXANDY/events{/privacy}","received_events_url":"https://api.github.com/users/XSLXANDY/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":2,"created_at":"2018-10-26T07:56:37Z","updated_at":"2018-10-27T02:44:56Z","closed_at":null,"author_association":"NONE","body":"Hello, I noticed you used L2 norm instead of F norm in your paper. \r\n![image](https://user-images.githubusercontent.com/29296287/47551921-03cc9280-d936-11e8-9fa6-399364dfcde6.png)\r\n![image](https://user-images.githubusercontent.com/29296287/47551955-13e47200-d936-11e8-80c9-f74e290ec835.png)\r\nIn the loss function, it seems the variable x and y are matrices because I haven't seen any sum operation.\r\nNotably, with regard to vectors, L2 norm is equal to F norm. But for matrix, L2 norm is totally different from F norm. \r\nI want to confirm whether you used L2 norm of the matrix because papers often use F norm rather than L2 norm in image denoising. \r\nCan you help me? Thanks a lot!"},"comment":{"url":"https://api.github.com/repos/GuoShi28/CBDNet/issues/comments/433584739","html_url":"https://github.com/GuoShi28/CBDNet/issues/7#issuecomment-433584739","issue_url":"https://api.github.com/repos/GuoShi28/CBDNet/issues/7","id":433584739,"node_id":"MDEyOklzc3VlQ29tbWVudDQzMzU4NDczOQ==","user":{"login":"GuoShi28","id":19340458,"node_id":"MDQ6VXNlcjE5MzQwNDU4","avatar_url":"https://avatars0.githubusercontent.com/u/19340458?v=4","gravatar_id":"","url":"https://api.github.com/users/GuoShi28","html_url":"https://github.com/GuoShi28","followers_url":"https://api.github.com/users/GuoShi28/followers","following_url":"https://api.github.com/users/GuoShi28/following{/other_user}","gists_url":"https://api.github.com/users/GuoShi28/gists{/gist_id}","starred_url":"https://api.github.com/users/GuoShi28/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/GuoShi28/subscriptions","organizations_url":"https://api.github.com/users/GuoShi28/orgs","repos_url":"https://api.github.com/users/GuoShi28/repos","events_url":"https://api.github.com/users/GuoShi28/events{/privacy}","received_events_url":"https://api.github.com/users/GuoShi28/received_events","type":"User","site_admin":false},"created_at":"2018-10-27T02:44:56Z","updated_at":"2018-10-27T02:44:56Z","author_association":"OWNER","body":"I am not sure I understand your question in the right way. preview works, such as [DnCNN](https://github.com/cszn/DnCNN), [FFDNet](https://github.com/cszn/FFDNet) are both using L2 norm. I did not do the experiment with F norm in denoising, so I can not say L2 norm is a better choice. \r\nThere a research discussed the impact of different loss functions in image restoration ([Paper](https://arxiv.org/pdf/1511.08861.pdf)). You can also refer to this paper and see whether its helpful to your understanding."}}
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