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Where is the parameter \gamma #2
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@happynear So if you look at our experiment configuration files, you can see we adopted an early stopping policy during the training process, i.e, we first train the network with DSN for a number of epochs (which is determined by validation) and we discard all the companion losses and continue to train the network with only the output loss. The gamma now is implicitly and dynamically determined by the loss value achieved at the time when we early stop, empirically this is essential for DSN to achieve good performance. |
In @happynear comment, \gamma is setted to prevent the hinge loss to be 0. |
@happynear @zhangliliang Sorry yes it is not "preventing hinge to be zero" but vanishing it. I assume it is a typo in original question? In our paper we have explained that: |
@s9xie I am working on implementing your method. So, you mean that you don't explicitly use Thanks, |
In formulation (3), there is a factor \gamma. This parameter is setted to prevent the hinge loss to be 0. However, I haven't find this parameter in the code.
The 0 loss is quite common in deep learning and this phenomenon is usually called "overfit". In deep learning, people usually use dropout to prevent the loss from getting to zero too early.
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