X1 fixed table

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@@ -73,8 +73,6 @@
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diff --git a/wAiFu.pdf b/wAiFu.pdf
index dddfae399c276d971bc3ca5cbe7f7f6f2370ea55..248f34ee41f4d6cb9584745096f08999171bfb1e 100644 (file)
Binary files a/wAiFu.pdf and b/wAiFu.pdf differ

diff --git a/wAiFu.tex b/wAiFu.tex
index 679da51484fa18b7c225dc29dbd5c6bb4f9e7e65..accd203d187ed7827936a9b4846e6c83fff94080 100644 (file)
--- a/wAiFu.tex
+++ b/wAiFu.tex
@@ -205,17 +205,17 @@ One of the main observations when training on such a small dataset was the tende
 \subsection{Error Rate of Thighs}
 The dataset containing 1000 images was labled using Hentai Tinder (Section~\ref{sec:impl_labelapp}) by three individual persons: User A, User B and User C. A table of the result for each user can be seen in Table~\ref{tab:user-table}. The three different users had varying rates of approval on the dataset with user C liking almost half of the dataset. The lowest error\_rate observed came from the dataset labled by user B. With the error\_rate being close to the rate of approval, a sanity check with a confusion matrix showed that the model did not just predict false on the whole dataset.
 
-\begin{table}
+\begin{table*}
 \centering
-\begin{tabular}{l|cccccc}
+\begin{tabular}{l|ccccccc}
     User & Approved & valid err & TP & FP & TN & FN & test err\\ \hline
-    A & 33.00\% & 26.87\% & 31 & 19 & 116 & 34 \\
-    B & 13.22\% & 22\% & 1 & 6 & 76 & 16 \\
-    C & 49.30\% & 28.36\% & 72 & 22 & 72 & 34 
+    A & 33.00\% & 26.87\% & 31 & 19 & 116 & 34 & \\
+    B & 13.22\% & 22.00\% & 1 & 6 & 76 & 16 & \\
+    C & 49.30\% & 28.36\% & 72 & 22 & 72 & 34 & 
 \end{tabular}
 \caption{User stats}
 \label{tab:user-table}
-\end{table}
+\end{table*}
 
 \section{Discussion} \label{sec:discussion}