I noticed that the weight of the loss layers are realised by setting the blob_lr parameter of the innerproduct layer in this project. This is equivalent to the formulation (3) for training the innerproduct layer. However, the gradients backpropagate to the bottom conv layer will not be influenced by the weight (0.001 in the prototxt file).
In another word, this realization just slowly learns the innerproduct layer of the previous SVMs, but applies the gradients of the classifiers equally to the nets. All SVMs have the same weight this way.
CAFFE has provided a param called "loss_weight", which is the correct method to realize the model described in the paper as far as I see.
This is all my opinion. If I were wrong, please reply me.
I noticed that the weight of the loss layers are realised by setting the blob_lr parameter of the innerproduct layer in this project. This is equivalent to the formulation (3) for training the innerproduct layer. However, the gradients backpropagate to the bottom conv layer will not be influenced by the weight (0.001 in the prototxt file).
In another word, this realization just slowly learns the innerproduct layer of the previous SVMs, but applies the gradients of the classifiers equally to the nets. All SVMs have the same weight this way.
CAFFE has provided a param called "loss_weight", which is the correct method to realize the model described in the paper as far as I see.
This is all my opinion. If I were wrong, please reply me.