Comments (2)
Hello @EmmaW8 , basically after the first stage, where a plain base model is trained, we use the plain model to calculate the class centers. Then in the beginning of the second stage, we use these centers to initialize the self.criterions['FeatureLoss'].centroids.data
, and from https://github.com/zhmiao/OpenLongTailRecognition-OLTR/blob/master/run_networks.py#L98 , we initialize an optimizer for the criterion. During training, in the self.batch_backward()
function (https://github.com/zhmiao/OpenLongTailRecognition-OLTR/blob/master/run_networks.py#L139), the criterion optimizer is stepped, and this is where the centroids are updated according to the losses, because centroids are updateable parameters of the feature criterion in stage 2. Does this make sense to you?
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@zhmiao Thank you for your detailed responce. :)
It is clear to me now~
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Related Issues (20)
- Implementation for methods cost sensitive, meta regression and meta model net? HOT 1
- Reproducing OLTR results HOT 3
- Stage 2 multi GPU
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- Unable to reproduce baseline result on ImageNet-LT HOT 1
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