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In you paper,
I am confused how to get v_*i?
Through gan inversion optimization to find the v_*i correspond to x_*i?
I can't find in your code, so sorry to bother you.
The text was updated successfully, but these errors were encountered:
So the (k*, v*) correspondence comes from the "copy-paste" action.
See the function rewrite.ganrewrite.ProgressiveGanRewriter.paste_from_selection. The output variable goal_in corresponds to k* and the variable goal-out is v*. Note that goal_in is the context_model (layer "L-1") representation of the target context, like a building spire, and the goal_out is the target_model (layer "L") representation of the replacement pattern, like a tree that we want to put in place instead of building spires. Note that we can get both representations through just inference in the GAN: no GAN inversion is needed.
In you paper,
I am confused how to get v_*i?
Through gan inversion optimization to find the v_*i correspond to x_*i?
I can't find in your code, so sorry to bother you.
The text was updated successfully, but these errors were encountered: