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I am using your approach to generate an initial latent and feature space for an hybrid approach.
During optimization, the loss I use for feature reconstruction is the same as the one used for training the encoder: F.mse_loss(features_in, features_out)
I am obtaining nearly perfect inversion but when I want to edit (only the latent space) I have results that are too close to the inverted. (Noting that I realized the edition of the features w.r.t formula 1 in your paper).
I am guessing that this might be because of the loss on feature reconstruction during inversion.
Do you have any suggestion for improving edition ?
Thanks
The text was updated successfully, but these errors were encountered:
Hi,
I am using your approach to generate an initial latent and feature space for an hybrid approach.
During optimization, the loss I use for feature reconstruction is the same as the one used for training the encoder: F.mse_loss(features_in, features_out)
I am obtaining nearly perfect inversion but when I want to edit (only the latent space) I have results that are too close to the inverted. (Noting that I realized the edition of the features w.r.t formula 1 in your paper).
I am guessing that this might be because of the loss on feature reconstruction during inversion.
Do you have any suggestion for improving edition ?
Thanks
The text was updated successfully, but these errors were encountered: