{"action":"reopened","issue":{"url":"https://api.github.com/repos/szagoruyko/wide-residual-networks/issues/17","repository_url":"https://api.github.com/repos/szagoruyko/wide-residual-networks","labels_url":"https://api.github.com/repos/szagoruyko/wide-residual-networks/issues/17/labels{/name}","comments_url":"https://api.github.com/repos/szagoruyko/wide-residual-networks/issues/17/comments","events_url":"https://api.github.com/repos/szagoruyko/wide-residual-networks/issues/17/events","html_url":"https://github.com/szagoruyko/wide-residual-networks/issues/17","id":178357333,"number":17,"title":"Data","user":{"login":"ibmua","id":2233511,"avatar_url":"https://avatars.githubusercontent.com/u/2233511?v=3","gravatar_id":"","url":"https://api.github.com/users/ibmua","html_url":"https://github.com/ibmua","followers_url":"https://api.github.com/users/ibmua/followers","following_url":"https://api.github.com/users/ibmua/following{/other_user}","gists_url":"https://api.github.com/users/ibmua/gists{/gist_id}","starred_url":"https://api.github.com/users/ibmua/starred{/owner}{/repo}","subscriptions_url":"https://api.github.com/users/ibmua/subscriptions","organizations_url":"https://api.github.com/users/ibmua/orgs","repos_url":"https://api.github.com/users/ibmua/repos","events_url":"https://api.github.com/users/ibmua/events{/privacy}","received_events_url":"https://api.github.com/users/ibmua/received_events","type":"User","site_admin":false},"labels":[],"state":"open","locked":false,"assignee":null,"assignees":[],"milestone":null,"comments":6,"created_at":"2016-09-21T14:07:06Z","updated_at":"2016-09-25T00:48:22Z","closed_at":null,"body":"Convolving over whitened, or just about anyhow prepossessed data (if the original data is not also fed) seemed like a bad idea to me, so I tried using the standard CIFAR-100 dataset simply scaled to [0..1]. And when I'm getting results that are even worse than you've published on your data, quite possibly, because of some differences in LR decrese functions and the like, I'm seeing results better than yours on on the usual dataset. For example, I'm seeing below 30% avg for 40|1 case for which you've published almost 31% and for which I'm getting around 32%.\r\n\r\nHave you even tried training on a standard data? I'm personally seeing around 2 point difference on 40|1 on Cifar-100 test. Considering that most of your results were in 2 point range and that's about the difference between 10m and 50m parameters in your nets, it makes a hell of a difference. \r\n\r\nOn my own architectures your preprocessing was even more destructive at least in many cases. Also, I've looked a bit for any mentions of preprocessing in the first paper (preact paper) you linked to where you tried to rationalize why you did it, couldn't find any mentions of whitening or any such preprocessing nor in it nor in https://arxiv.org/pdf/1512.03385v1.pdf that they've linked to when they've said they use data like in this paper. \r\n\r\nAlso, in http://cs231n.github.io/neural-networks-2/#datapre whitening of images is discouraged, though they don't talk about that particular type of of whitening."}}
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