boyiliee / sitta Goto Github PK

Dear @Boyiliee ,

Thanks for sharing the great work.

when the code will be released?

Best regards,
Jun

Hello! very great project! Here I wonder can the texture latent vector be altered by disturbing the texture latent vector, instead of generating the texture latent vector using the Texture Encoder in your model. In this way, there is no need to find the image pairs that contain the same objects like birds, and people can arbitrarily change the textures of a singe image.

Dear @Boyiliee ,

Thanks for sharing the code.

Would it be possible for you to provide some demo data to illustrate how to use the code with a new dataset?

Dear @Boyiliee ,
I am trying to implement your paper in code.
Some hyperparameters are omitted from the paper , so it is difficult to implement.
Could you please tell me batch size and lambda values ​​of loss?
Thank you.

According to the paper, Kl divergence loss is computed between textures t_A, t_ba and ** t_B, t_ab**, however the computation:

loss_netG_A_texture = -0.5 * (F.kl_div(t_A, t_ba) + F.kl_div(t_ba, t_A))
loss_netG_B_texture = -0.5 * (F.kl_div(t_B, t_ab) + F.kl_div(t_ab, t_B))

Looks more like the JS divergence but with a negative sign.

Also the inputs for the Kl divergence loss are supposed to be in the log softmax space but the textures t_A, t_ba, t_B, t_ab come from the ReLU "space", is this the reason behind the negative sign in the equation? or am I losing some detail about the implementation?