yenchanghsu / generalized-odin Goto Github PK

Hi,

I have read your paper. The divisor/dividend approach is very interesting. I have a question regarding the dropout regularization that you mentioned in the paper. It says in the paper that 'apply dropout at the inputs for the dividend/divisor structure.'. By this you mean the dropout is applied to the input x itself or to the f_p(x)? btw is there any implementation of your work that I can refer to?

thanks

Hey, I loved the paper and I'm very interested in your work here. Is there a timeline for when the code might become available?

what is transformation of out of distribution, resize to what, and how does it crop?

Can you share the link to where you download the out of distribution images datasets?

Hello,

I am working on a re-implementation of this work. What portion of the train data is segmented for the val dataset? My implementation has 15% right now, but that's just a guess.

Did you used standard resnet and densenet with standard input size (224 x 224)? or did you use modified version of this networks with different input size?