Perceptual loss¶
The identification of content and style are core elements of a Neural Style Transfer
(NST). The agreement of the content and style of two images is measured with the
content_loss and style_loss, respectively.
Operators¶
In pystiche these losses are implemented Loss s.
Loss s are differentiated between two types:
RegularizationLoss and
ComparisonLoss. A
RegularizationLoss works without any context while a
ComparisonLoss compares two images. Furthermore,
pystiche differentiates between two different domains an
Loss can work on: PixelOperator
and EncodingOperator . A PixelOperator
operates directly on the input_image while an
EncodingOperator encodes it first.
In total pystiche supports four archetypes:
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Multi-layer encoder¶
One of the main improvements of NST compared to traditional approaches is that the
agreement is not measured in the pixel or a handcrafted feature space, but rather in
the learned feature space of a Convolutional Neural Network called encoder.
Especially variants of the style_loss depend upon encodings, i. e. feature maps,
from various layers of the encoder.
pystiche offers a
MultiLayerEncoder that enables to extract all required encodings
after a single forward pass. If the same operator should be applied to different layers
of a MultiLayerEncoder, a
MultiLayerEncodingLoss can be used.
Perceptual loss¶
The PerceptualLoss combines all Operator
s in a single measure acting as joint optimization criterion. How the optimization is
performed will be detailed in the next section.