lukas-heilgenbrunner
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17 lines
848 B
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17 lines
848 B
XML
= Implementation
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== Experiment Setup
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% todo
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todo setup of experiments, which classes used, nr of samples
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kinds of experiments which lead to graphs
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== Jupyter
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To get accurate performance measures the active-learning process was implemented in a Jupyter notebook first.
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This helps to choose which of the methods performs the best and which one to use in the final Dagster pipeline.
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A straight forward machine-learning pipeline was implemented with the help of Pytorch and RESNet-18.
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Moreover, the Dataset was manually imported with the help of a custom torch dataloader and preprocessed with random augmentations.
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After each loop iteration the Area Under the Curve (AUC) was calculated over the validation set to get a performance measure.
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All those AUC were visualized in a line plot, see section~\ref{sec:experimental-results} for the results.
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