CODEWITHSUNDEEP
machine-learningneural-networkdeep-learningcaffe
user824624
user824624

Reputation: 8078

how to rescale the weight of the feature map in caffe?

In the paper of 'Inside-Outside Net: Detecting Objects in Context with Skip Pooling and Recurrent Neural Networks'

they are using the skip connection concept to concat roi-pooled features of the layer conv3, conv4, conv5, but before the concatenation, they propose to use L2 NORM and rescale each feature map extracted from these layers, the question to me is how to determine the re-scaling values for the pooled feature, which caffe layer can be used to implement this ?

Upvotes: 0

Views: 268

Answers (1)

Shai
Shai

Reputation: 114816

You can use existing layers to compute L2 norm of a feature map. See this thread for and example.
You can use "Scale" layer to scale each feature map.

Upvotes: 2

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