Comments (4)
@jchhuang Modulated attention depends largely on a good initial feature map. If we use attention with randomly initialized weights, the results will be much lower because the attention won't work properly. Same as meta embedding which relies on a good initialized memory. If the memory is randomly initialized, the whole training process will break. Cosine classifier is embedded in meta emdedding classifier so won't be used in stage 1.
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@zhmiao Thanks for your reply. For my understanding, memory isn't trained by the stage-1,but initialed by the results of stage-1 at the beginning stage-2, and finally updated by the vmeta, does it?
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@jchhuang Yes, memory is not trained at stage1. However, without any trained weights, there is no proper feature space that can generate a good memory, which is constructed as feature centroids of each class. Once stage1 is finished, we can use the pretrained weights to initialize the memory, because the feature space is not random any more.
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Thanks for your good explain!
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Related Issues (20)
- Reproducing OLTR results HOT 3
- Stage 2 multi GPU
- why fix all parameters except self attention parameters? HOT 4
- Table 2 results HOT 2
- Pretrained Weights for Places_LT?
- the use of fc layer HOT 2
- the accuracy of the train and val HOT 2
- how to compute centroids?
- Why the input dimension of the `fc_spatial` layer in `ModulatedAttLayer` is 7*7*in_channel? HOT 1
- Many_shot_accuracy_top1: nan on my own dataset HOT 1
- Revised F-measure results for other models in your paper
- Applications for face recognition
- Error when running stage_1.py under Places_LT
- Unable to reproduce baseline result on ImageNet-LT HOT 1
- BUG: stage1 test error!!
- Could you please give me an example of arranging ILSVRC2014 dataset? HOT 7
- Implementation on Inat-18
- About Class aware sampler
- The role of untrained FC(add_fc)
- The question about the version of Places_LT
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