Commit 56b185ac authored by Alexander Henkel's avatar Alexander Henkel
Browse files

finished state to review

parent 1e3db523
......@@ -9,7 +9,7 @@
\caption[Personalization evolution single]{\textbf{Personalization evolution single.} Graph shows the iterative evaluation of personalized model after each iteration step. Additionally evaluation of the base model just trained with the single training data of the respective step is drawn.}
\caption[Personalization evolution single]{\textbf{Personalization evolution single.} Graph shows the iterative evaluation of personalized model after each iteration step. Additionally the evaluation of the base model just trained with the single training data of the respective step is plotted.}
\label{fig:evolutionSingle}
\end{centering}
......
......@@ -6,7 +6,7 @@
\subfloat[Increasing $c\%$]
{\includegraphics[width=\textwidth]{figures/experiments/supervised_pseudo_missing_hw.png}}
\caption[Pseudo models missing feedback]{\textbf{Pseudo models missing feedback} Evaluation of personalized models with incomplete feedback.}
\caption[Pseudo models missing feedback]{\textbf{Pseudo models missing feedback.} Evaluation of personalized models with incomplete feedback. In (a), all \textit{correct} are given and the ratio of \textit{false} feedback is changed. In (b) all \textit{false} indicators are given and the ratio of \textit{correct} feedback is changed.}
\label{fig:supervisedPseudoMissingFeedback}
\end{centering}
\end{figure}
......@@ -7,7 +7,7 @@
{\includegraphics[width=\textwidth]{figures/experiments/supervised_pseudo_models_f1_s.png}}
\caption[Pseudo models evaluation]{\textbf{Pseudo models evaluation} Measured metrics on models which have been trained on different filter configurations of training data labels. In plot of S score, the evaluation of respective training data is included. The scaled evaluations part is scaled to the ratio of training data labels to ground truth labels.}
\caption[Pseudo models evaluation]{\textbf{Pseudo models evaluation.} Measured metrics on models which have been trained on different filter configurations of training data labels. In plot of S score, the evaluation of respective training data is included. The scaled evaluations part is scaled to the ratio of training data labels to ground truth labels.}
\label{fig:pseudoModelsEvaluation}
\end{centering}
\end{figure}
......@@ -7,7 +7,7 @@
{\includegraphics[width=\textwidth]{figures/experiments/supervised_pseudo_models_training_data_confusion.png}}
\caption[Pseudo models training data]{\textbf{Pseudo models training data} Analysis of training data which was used to train previous models. Brighter parts in (a) and values in (b) are scaled to the respective ratio of training data labels to ground truth labels.}
\caption[Pseudo models training data]{\textbf{Pseudo models training data.} Analysis of training data which was used to train previous models. Brighter parts in (a) and values in (b) are scaled to the respective ratio of training data labels to ground truth labels.}
\label{fig:pseudoModelsTrainingData}
\end{centering}
\end{figure}
......@@ -6,7 +6,7 @@
\subfloat[Increasing $n$]
{\includegraphics[width=\textwidth]{figures/experiments/supervised_random_noise_null_all_f1_s.png}}
\caption[Supervised noisy training hw]{\textbf{Supervised noisy training hw} Graphs shows the F1 score (left) and S score (right) of personalized models which are trained on increasing values of noise. The mean is computed over all personalizations with same noise value. In (a) noise is applied to hand wash labels and in (b) to \textit{null} labels.}
\caption[Supervised noisy training scores]{\textbf{Supervised noisy training scores} Graphs shows the F1 score (left) and S score (right) of personalized models which are trained on increasing values of noise. The mean is computed over all personalizations with same noise value. In (a) noise is applied to hand wash labels and in (b) to \textit{null} labels.}
\label{fig:supervisedNoisyAllF1S}
\end{centering}
\end{figure}
......@@ -6,7 +6,7 @@
\subfloat[Increasing $n$]
{\includegraphics[width=\textwidth]{figures/experiments/supervised_random_noise_null_all_spec_sen.png}}
\caption[Supervised noisy training hw]{\textbf{Supervised noisy training hw} Graphs shows the specificity (left) and sensitivity (right) of personalized models which are trained on increasing values of noise. The mean is computed over all personalizations with same noise value. In (a) noise is applied to hand wash labels and in (b) to \textit{null} labels.}
\caption[Supervised noisy training]{\textbf{Supervised noisy training.} Graphs shows the specificity (left) and sensitivity (right) of personalized models which are trained on increasing values of noise. The mean is computed over all personalizations with same noise value. In (a) noise is applied to hand wash labels and in (b) to \textit{null} labels.}
\label{fig:supervisedNoisyAllSpecSen}
\end{centering}
\end{figure}
......@@ -6,7 +6,7 @@
\subfloat[Increasing $n$]
{\includegraphics[width=\textwidth]{figures/experiments/supervised_random_noise_null_part_f1_s.png}}
\caption[Supervised noisy training part]{\textbf{Supervised noisy training part} Graphs show evaluation on personalizations trained with increasing noise on \textit{null} labels. Values of $n$ are focused to the first $1\%$.}
\caption[Supervised noisy training part]{\textbf{Supervised noisy training part.} Graphs show evaluation on personalizations trained with increasing noise on \textit{null} labels. Values of $n$ are focused to the first $1\%$.}
\label{fig:supervisedNoisyPart}
\end{centering}
\end{figure}
......@@ -20,6 +20,6 @@
\caption[Active learning evaluation]{\textbf{Active learning evaluation} Best hyper parameter settings for active learning according the S score of resulting model.}
\caption[Active learning evaluation]{\textbf{Active learning evaluation.} Best hyper parameter settings for active learning, according to the S score of the resulting model.}
\label{tab:activeLearningEvaluation}
\end{table}
......@@ -20,6 +20,6 @@
\caption[Active learning grid search]{\textbf{Active learning grid search} }
\caption[Active learning grid search]{\textbf{Active learning grid search.} Hyper-Parameters and their values which have been tested for the active learning approach.}
\label{tab:activeLearningGridSearch}
\end{table}
......@@ -8,16 +8,18 @@
%\toprule
\thead{participant} & \thead{recordings} & \thead{samples} & \thead{hours} & \thead{manual\\indicator} & \thead{correct\\indicator} & \thead{false\\indicator} & \thead{neutral\\indicator} \\
\midrule
OCDetect\_02 & 13 & 19398547 & 107 & 7 & 81 & 65 & 112 \\
OCDetect\_03 & 7 & 19333998 & 107 & 36 & 77 & 12 & 27 \\
OCDetect\_04 & 15 & 30371163 & 168 & 38 & 56 & 0 & 39 \\
OCDetect\_05 & 27 & 45313733 & 251 & 105 & 243 & 17 & 949 \\
OCDetect\_07 & 11 & 13178338 & 73 & 11 & 23 & 4 & 7 \\
OCDetect\_09 & 11 & 39808808 & 221 & 42 & 43 & 7 & 77 \\
OCDetect\_10 & 10 & 8387805 & 46 & 1 & 8 & 0 & 119 \\
OCDetect\_11 & 16 & 40522845 & 225 & 45 & 56 & 38 & 26 \\
OCDetect\_12 & 13 & 8299920 & 46 & 72 & 73 & 0 & 5 \\
OCDetect\_13 & 15 & 33018908 & 183 & 21 & 42 & 14 & 39 \\
OCDetect\_02 & 13 & 19398547 & 107 & 7 & 74 & 65 & 112 \\
OCDetect\_03 & 7 & 19333998 & 107 & 36 & 41 & 12 & 27 \\
OCDetect\_04 & 23 & 37175570 & 206 & 57 & 35 & 1 & 53 \\
OCDetect\_05 & 27 & 45313733 & 251 & 105 & 138 & 17 & 949 \\
OCDetect\_07 & 11 & 13178338 & 73 & 11 & 12 & 4 & 7 \\
OCDetect\_09 & 10 & 35034335 & 194 & 37 & 0 & 7 & 72 \\
OCDetect\_10 & 10 & 8387805 & 46 & 1 & 7 & 0 & 119 \\
OCDetect\_11 & 19 & 46397570 & 257 & 53 & 11 & 39 & 35 \\
OCDetect\_12 & 13 & 8299920 & 46 & 72 & 1 & 0 & 5 \\
OCDetect\_13 & 15 & 33018908 & 183 & 21 & 21 & 14 & 39 \\
OCDetect\_18 & 8 & 12937161 & 71 & 47 & 20 & 9 & 17 \\
OCDetect\_20 & 14 & 29443317 & 163 & 9 & 179 & 64 & 69 \\
\bottomrule
\end{tabular}}}
......@@ -27,20 +29,22 @@
%\toprule
\thead{participant} & \thead{recordings} & \thead{samples} & \thead{hours} & \thead{manual\\indicator} & \thead{correct\\indicator} & \thead{false\\indicator} & \thead{neutral\\indicator} \\
\midrule
OCDetect\_02 & 6 & 10861527 & 60 & 9 & 52 & 41 & 71 \\
OCDetect\_03 & 8 & 21251292 & 118 & 42 & 87 & 11 & 65 \\
OCDetect\_04 & 5 & 10668781 & 59 & 18 & 33 & 0 & 22 \\
OCDetect\_05 & 10 & 19345852 & 107 & 52 & 117 & 6 & 538 \\
OCDetect\_07 & 4 & 4813866 & 26 & 4 & 15 & 7 & 9 \\
OCDetect\_09 & 4 & 13724780 & 76 & 10 & 20 & 14 & 92 \\
OCDetect\_10 & 2 & 2243083 & 12 & 1 & 9 & 0 & 193 \\
OCDetect\_11 & 5 & 15818377 & 87 & 25 & 35 & 14 & 20 \\
OCDetect\_12 & 5 & 6502526 & 36 & 76 & 76 & 0 & 1 \\
OCDetect\_13 & 6 & 16679159 & 92 & 11 & 30 & 15 & 37 \\
OCDetect\_02 & 6 & 10861527 & 60 & 9 & 43 & 41 & 71 \\
OCDetect\_03 & 8 & 21251292 & 118 & 42 & 45 & 11 & 65 \\
OCDetect\_04 & 5 & 10668781 & 59 & 18 & 15 & 0 & 22 \\
OCDetect\_05 & 10 & 19345852 & 107 & 52 & 65 & 6 & 538 \\
OCDetect\_07 & 4 & 4813866 & 26 & 4 & 11 & 7 & 9 \\
OCDetect\_09 & 4 & 13724780 & 76 & 10 & 10 & 14 & 92 \\
OCDetect\_10 & 2 & 2243083 & 12 & 1 & 8 & 0 & 193 \\
OCDetect\_11 & 5 & 15818377 & 87 & 25 & 10 & 14 & 20 \\
OCDetect\_12 & 5 & 6502526 & 36 & 76 & 0 & 0 & 1 \\
OCDetect\_13 & 6 & 16679159 & 92 & 11 & 19 & 15 & 37 \\
OCDetect\_18 & 4 & 8249562 & 45 & 40 & 30 & 12 & 22 \\
OCDetect\_20 & 4 & 7162813 & 39 & 13 & 47 & 20 & 12 \\
\bottomrule
\end{tabular}}}
\caption[Real world dataset]{\textbf{Real world dataset} Overview of used datasets for real world experiments. Recordings for test split have been selected by hand. They have been chosen because they cover a wider variety of user feedback.}
\caption[Real world dataset]{\textbf{Real world dataset.} Overview of used datasets for real world experiments. Recordings for test split have been selected by hand. They have been chosen because they cover a wider variety of user feedback.}
\label{tab:realWorldDataset}
\end{table}
......@@ -23,6 +23,6 @@
\end{adjustbox}
\caption[Real world evaluation]{\textbf{Real world evaluation} Summary of quality estimation over multiple participants. The personalization with highest F1 score per participant is shown.}
\caption[Real world evaluation]{\textbf{Real world evaluation.} Summary of quality estimation over multiple participants. The personalization with highest F1 score per participant is shown.}
\label{tab:realWorldEvaluation}
\end{table}
......@@ -6,22 +6,24 @@
\resizebox{\textwidth}{!}{%
\begin{tabular}{lrrrr}
\toprule
\thead{participant} & \thead{sum\\hand washes} & \thead{sum correct \\ hand washes} & \thead{sum false \\ hand washes} & \thead{f1} \\
\thead{participant} & \thead{true\\hand washes} & \thead{predicted correct \\ hand washes} & \thead{predicted false \\ hand washes} & \thead{F1} \\
\midrule
OCDetect\_02 & 68 & 55 & 175 & 0.3691 \\
OCDetect\_03 & 97 & 66 & 183 & 0.3815 \\
OCDetect\_04 & 39 & 17 & 19 & 0.4533 \\
OCDetect\_05 & 220 & 90 & 307 & 0.2917 \\
OCDetect\_07 & 16 & 13 & 14 & 0.6047 \\
OCDetect\_09 & 26 & 11 & 77 & 0.1930 \\
OCDetect\_10\_2 & 17 & 7 & 134 & 0.0886 \\
OCDetect\_11 & 38 & 9 & 26 & 0.2466 \\
OCDetect\_12 & 77 & 32 & 13 & 0.5246 \\
OCDetect\_13 & 46 & 20 & 62 & 0.3125 \\
OCDetect\_02 & 68 & 55 & 175 & 0.3691 \\
OCDetect\_03 & 97 & 66 & 183 & 0.3815 \\
OCDetect\_04 & 39 & 17 & 18 & 0.4595 \\
OCDetect\_05 & 220 & 90 & 307 & 0.2917 \\
OCDetect\_07 & 16 & 13 & 14 & 0.6047 \\
OCDetect\_09 & 26 & 11 & 77 & 0.1930 \\
OCDetect\_10 & 17 & 7 & 134 & 0.0886 \\
OCDetect\_11 & 38 & 9 & 24 & 0.2535 \\
OCDetect\_12 & 77 & 32 & 13 & 0.5246 \\
OCDetect\_13 & 46 & 20 & 61 & 0.3150 \\
OCDetect\_18 & 83 & 42 & 22 & 0.5714 \\
OCDetect\_20 & 64 & 50 & 24 & 0.7246 \\
\bottomrule
\end{tabular}}
\caption[General model evaluation]{\textbf{General model evaluation} Evaluation of the general model to the test sets of real world experiment.}
\caption[General model evaluation]{\textbf{General model evaluation.} Quality estimation of the general model to the test sets of real world experiment.}
\label{tab:realWorldGeneralEvaluation}
\end{table}
......@@ -181,3 +181,5 @@
\renewcommand\theadfont{\bfseries}
\renewcommand\theadgape{\Gape[4pt]}
\renewcommand\cellgape{\Gape[4pt]}
\newcommand{\STAB}[1]{\begin{tabular}{@{}c@{}}#1\end{tabular}}
......@@ -51,7 +51,7 @@
\tableofcontents
\listoffigures
\listoftables
\listofalgorithms
%\listofalgorithms
\hypersetup{pageanchor=true} % re-enable hyperlinking
\mainmatter % Arabic page numbers
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