{"action":"opened","issue":{"url":"https://api.github.com/repos/dmlc/mxnet/issues/1112","labels_url":"https://api.github.com/repos/dmlc/mxnet/issues/1112/labels{/name}","comments_url":"https://api.github.com/repos/dmlc/mxnet/issues/1112/comments","events_url":"https://api.github.com/repos/dmlc/mxnet/issues/1112/events","html_url":"https://github.com/dmlc/mxnet/issues/1112","id":124323909,"number":1112,"title":"Samples with different size","user":{"login":"pevnak","id":16486978,"avatar_url":"https://avatars.githubusercontent.com/u/16486978?v=3","gravatar_id":"","url":"https://api.github.com/users/pevnak","html_url":"https://github.com/pevnak","followers_url":"https://api.github.com/users/pevnak/followers","following_url":"https://api.github.com/users/pevnak/following{/other_user}","gists_url":"https://api.github.com/users/pevnak/gists{/gist_id}","starred_url":"https://api.github.com/users/pevnak/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/pevnak/subscriptions","organizations_url":"https://api.github.com/users/pevnak/orgs","repos_url":"https://api.github.com/users/pevnak/repos","events_url":"https://api.github.com/users/pevnak/events{/privacy}","received_events_url":"https://api.github.com/users/pevnak/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"milestone":null,"comments":0,"created_at":"2015-12-30T09:20:12Z","updated_at":"2015-12-30T09:20:12Z","closed_at":null,"body":"Hi,\r\nI have a question related to use of mxnet, particularly if I can tweak it to my problem. I want to use the library for multi-instance learning, which means that each sample is composed of multiple instances, but the number of instances in each sample differs from sample to sample. You can imagine this as a 2D image, where each image has different height, but the width of all images is the same.\r\nWhen the pooling operation is performed over the height, then we get sample of fixed size and one can follow the usual Neural Nets, where each sample has the same size. \r\nWhat I feel is that the usual data provider cannot be used here, as it assumes that all samples are of fixed size. Am I right?\r\nIs there a way, how this can be fixed? "}}