T2Net
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incompatibility in code and article
In your article after concatenating Q and output of the transfer attention layer, we see two convolution layers as illustrated in Fig 2
,however, In code you applied one Conv why do not they match?
T=self.conv1[i](T)
Hi Maral, Thanks for your email. We are sorry that the paper is inconsistent with the code. However, there's no big difference between one convolution layer and two layers.
Maral @.***> 于2022年8月22日周一 23:32写道:
In your article after concatenating Q and output of the transfer attention layer, we see two convolution layers as illustrated in Fig 2 ,however, In code you applied one Conv why do not match? T=self.conv1i
— Reply to this email directly, view it on GitHub https://github.com/chunmeifeng/T2Net/issues/12, or unsubscribe https://github.com/notifications/unsubscribe-auth/AR75XN6RMROCRATB4A3M75LV2PWXRANCNFSM57JFIUOQ . You are receiving this because you are subscribed to this thread.Message ID: @.***>
Thank you for the quick response and also amazing research. So the results of the articles are based on two convs ? ??
All the results are obtained from our released code.
Maral @.***> 于2022年8月23日周二 00:36写道:
Thank you for the quick response and also amazing research. So the results of the articles are based on two convs ? ??
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Thank you.