{"action":"opened","issue":{"url":"https://api.github.com/repos/dmlc/mxnet/issues/1412","repository_url":"https://api.github.com/repos/dmlc/mxnet","labels_url":"https://api.github.com/repos/dmlc/mxnet/issues/1412/labels{/name}","comments_url":"https://api.github.com/repos/dmlc/mxnet/issues/1412/comments","events_url":"https://api.github.com/repos/dmlc/mxnet/issues/1412/events","html_url":"https://github.com/dmlc/mxnet/issues/1412","id":131190765,"number":1412,"title":"I dont quite understand how Upsampling works.","user":{"login":"ascust","id":12552820,"avatar_url":"https://avatars.githubusercontent.com/u/12552820?v=3","gravatar_id":"","url":"https://api.github.com/users/ascust","html_url":"https://github.com/ascust","followers_url":"https://api.github.com/users/ascust/followers","following_url":"https://api.github.com/users/ascust/following{/other_user}","gists_url":"https://api.github.com/users/ascust/gists{/gist_id}","starred_url":"https://api.github.com/users/ascust/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ascust/subscriptions","organizations_url":"https://api.github.com/users/ascust/orgs","repos_url":"https://api.github.com/users/ascust/repos","events_url":"https://api.github.com/users/ascust/events{/privacy}","received_events_url":"https://api.github.com/users/ascust/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2016-02-03T23:40:11Z","updated_at":"2016-02-03T23:40:11Z","closed_at":null,"body":"I am trying to implement a fully convolutional neural network which requires an upsampling step before softmaxout. The small score maps need to be upsampled to the same size as the ground-truth label. I tried to use Upsampling layer but it seems the layer requires weights parameters. I simply just want to use bilinear methods to scale the score maps, for example I have 21 score maps with size 64*64. I just need them to be resized to for example 128*128. I dont see the need of those weights. Could anyone explain to me how this Upsampling layer works? If this layer is not what I am looking for, what else can I use to achieve this?"}}