torch-dreams
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add support for noisegrad
The paper can be found here: https://arxiv.org/pdf/2106.10185.pdf The core idea is to add noise to the weights on each iteration, in proportion to the weights variance in each layer.
It can be added in as an argument in the render() method:
image_param = dreamy_boi.render(
layers = [model.Mixed_5b],
use_noisegrad = True
)
The process is formally described below :arrow_down: :

Fast Noisegrad: The idea is just to add multiplicative noise to the model weights on each optimisation iteration -- but each time to the original model weights. Thus not stacking up any noise.