Add Trainable Domain Adaptation workflow #2704
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For a given multichannel input image, this operator selects a subset of channel and presents them at the Output.
initial version of workflow implementing https://www.frontiersin.org/articles/10.3389/fcomp.2022.805166/full https://www.biorxiv.org/content/10.1101/2021.11.09.467925v1
emilmelnikov
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May 5, 2023
ilastik/applets/trainableDomainAdaptation/opTrainableDomainAdaptation.py
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ilastik/applets/trainableDomainAdaptation/trainableDomainAdaptationApplet.py
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ilastik/applets/trainableDomainAdaptation/trainableDomainAdaptationGui.py
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ilastik/applets/trainableDomainAdaptation/trainableDomainAdaptationGui.py
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ilastik/applets/trainableDomainAdaptation/trainableDomainAdaptationGui.py
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ilastik/applets/trainableDomainAdaptation/trainableDomainAdaptationGui.py
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* added separator in model combo for more visual clarity * simplified device selection * fixed/simplified `handleAppletStateUpdateRequested` * allow export of "Enhancer" output * some syntactic simplifications * some cosmetic changes Co-Authored-By: Emil Melnikov <emilmelnikov@users.noreply.github.com>
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thx a lot @emilmelnikov for the review! |
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aka shallow2deep
Not a lot of ways to make this PR more digestible - it is a whole new workflow after all. The only added applet would be
ilastik.applets.trainableDomainAdaptation.Lets you
The implementation relies a lot on the functionality (tried and tested) of the Pixel Classification Workflow, and the Neural Network Classification Workflow.
Ref: From Shallow to Deep: Exploiting Feature-Based Classifiers for Domain Adaptation in Semantic Segmentation | preprint