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\BOOKMARK [2][-]{subsection.2.1}{Artificial Intelligence}{section.2}% 3
\BOOKMARK [2][-]{subsection.2.2}{Hentai and Thighdeology}{section.2}% 4
\BOOKMARK [1][-]{section.3}{Method}{}% 5
-\BOOKMARK [2][-]{subsection.3.1}{Data Collection}{section.3}% 6
-\BOOKMARK [2][-]{subsection.3.2}{Data Transformation}{section.3}% 7
-\BOOKMARK [2][-]{subsection.3.3}{Data Labeling}{section.3}% 8
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-\BOOKMARK [1][-]{section.4}{Design}{}% 10
-\BOOKMARK [2][-]{subsection.4.1}{wAiFu Framework}{section.4}% 11
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\BOOKMARK [2][-]{subsection.5.1}{Data Transformations}{section.5}% 13
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\BOOKMARK [3][-]{subsubsection.5.1.2}{Cropping Images}{subsection.5.1}% 15
\BOOKMARK [2][-]{subsection.5.2}{Label App: Hentai Tinder}{section.5}% 16
-\BOOKMARK [2][-]{subsection.5.3}{Deep Learning with fast.ai}{section.5}% 17
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-\BOOKMARK [2][-]{subsection.6.2}{Future Work}{section.6}% 20
-\BOOKMARK [1][-]{section.7}{Conclusion}{}% 21
-\BOOKMARK [1][-]{section*.2}{References}{}% 22
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\maketitle
\begin{abstract}
-In this paper, we propose a framework for collecting data, labeling data, and training machine learning models within the area of lewd anime/manga and hentai.
+ For too many years have the world of Artificial Intelligence and the world of Hentai been separate ecosystems in which they do not realize the powerful potential of an alliance. Project Hentai AI aims to bring Artificial Intelligence into the sphere of Hentai, Ecchi and Lewds. In this paper, we propose a Witty Artificial Intelligence Framework Utilization (wAiFu). This framework is built for collecting data, labeling data, and training machine learning models to rate images of lewd anime/manga and hentai. As a proof of concept, this framework is applied to lewd anime thighs. A dataset is collected, transformed and labeled before being loaded into a fastai implementation of a Convolutional Neural Network designed for Computer Vision. The retraining of a resnet34 model for 10 epoch resulted in an accuracy of 70\%, which is much better than a cointoss.
\end{abstract}
\begin{IEEEkeywords}
\section{Introduction} \label{sec:intro}
It all began when a friend started reviewing anime thighs sent their way. The reviews were simply approved or disapproved, but the surprisingly low amount of approved images sparked the idea of a machine learning model capable of learning an individual's taste in anime thighs.
-
-\emph{Project Hentai AI: wAiFu} is only one of many future projects planned within Project Hentai AI. The framework of wAiFu is planned to be utilized beyond thighs in the future, and extend into other hentai areas (e.g., tits, ass, abs, middriffs and armpits).
+% Add more here
+The framework of wAiFu is not limited to lewd anime thighs, but can very easily be extended to other areas e.g., tits, ass, abs, middriffs and armpits.
\section{Background} \label{sec:background}
\emph{``A computer program is said to learn from experience E with respect to some class of tasks T and performance measure P if its performance at tasks in T, as measured by P, improves with experience E''}
\end{quote}
This means that the algorithm gain experience by training on a task and that this training can then be measured. And the more the algorithm train and gain experience, the better it performs on the task. These tasks are usually classification tasks in ML (e.g., classify email as spam or separating images of cats from images of dogs).
-% Todo: Add something about neural networks?
While ML needs to perform the feature extraction manually from the input before classification, \emph{Deep Learning} (DL) neural networks automatically extracts the features as a part of the classification \cite{deeplearning}. DL also uses backpropagation algorithms to adjust the parameters of hidden layers (between the input and output layers) during training. Due to its feature extraction, DL can work on both structured and unstructured data as input, and this in turn has made DL efficient in object detection and speech recognition, both of which are classification problems (e.g., does the \emph{sound} match any known \emph{word}).
\section{Method} \label{sec:method}
+\subsection{wAiFu Framework} \label{sec:waifu}
+Witty Artificial Intelligence Framework Utilization.
+% Talk about the overview of the framework, the main idea
+
+\section{Design} \label{sec:design}
+
\subsection{Data Collection} \label{sec:datacollection}
\noindent The data was collected manually from six separate sources:
\begin{itemize}
\item Discord Server: Hanako's Hideout\footnote{formerly known as r/Hentai Group prior to 13th April 2021}
\item Discord Server: hanime.tv Community
\item Discord Server: NCE: The NEKOPARA Community
- \item Subreddit: Thighdeology\footnote{\url{https://www.reddit.com/r/thighdeology/}}
+ \item Subreddit: Thighdeology
\item Private Donations
\end{itemize}
The data transformation implementation is detailed in Section~\ref{sec:datatfms}.
\subsection{Data Labeling} \label{sec:datalabeling}
-The labeling of datasets in wAiFu is categorised in three different methods:
+The labeling of datasets in wAiFu is categorised in two different methods:
\begin{itemize}
\item Boolean labeling
\item Score labeling
- \item Multi-labeling
\end{itemize}
The \emph{Boolean labeling} consist of two disjunctive values (e.g., True/False, Yes/No, Approved/Disapproved, 1/0) which is the closest to the reviews previously gotten when brokering pictures of anime thighs manually. An image would be sent and an Approved/Disapproved would be received in return. A diagram example is seen in Figure~\ref{fig:protocol}.
-\begin{figure}
+
+\begin{figure}[h]
\includegraphics[width=.5\textwidth]{img/thighs_diagram.drawio.pdf}
\caption{The protocol of reviewing thighs using boolean labeling}
\label{fig:protocol}
\end{figure}
-The \emph{Score labeling} ranks the images on a scale (e.g., 0-10, 1-5, A-F). This could be considered to be a more advanced implementation of Boolean labeling (which would be viewed as a scale of 0-1) by adding more values in between.
-
-The \emph{Multi-labeling} is an additional application area outside of just ranking thighs. Tags could be marked as labels (multiple labels per image) in order to recognise and identify these patterns. This could be related to clothes (e.g., thigh highs, panties, skirt) or body features (e.g., muscle, tattoo, tanned).
+The \emph{Score labeling} ranks the images on a scale (e.g., 0-10, 1-5, A-F). This could be considered to be a more advanced implementation of Boolean labeling (which would be viewed as a scale of 0-1) by adding float values in between.
The data labeling implementation is detailed in Section~\ref{sec:impl_labelapp}
-\subsection{fast.ai} \label{sec:fastai}
-% Todo
-The AI implementation was using fast.ai, a layered API for deep learning~\cite{fastai}.
-
-\section{Design} \label{sec:design}
-
-\subsection{wAiFu Framework} \label{sec:waifu}
-Witty Artificial Intelligence Framework Utilization.
-% Talk about the overview of the framework, the main idea
+\subsection{fastai} \label{sec:fastai}
+% Todo What is fastai
+The AI implementation was using fastai, a layered API for deep learning~\cite{fastai}.
\section{Implementation} \label{sec:implementation}
The code of all tools in Project Hentai AI is open source and can be found at \url{https://git.hentai-ai.org}.
\subsection{Data Transformations} \label{sec:datatfms}
+The following section goes through the implementation of homogenizing the dataset, including renaming, changing extensions and cropping the images.
\subsubsection{Convert and Rename}
+% Add git link
Talk about the script for making the dataset homogeneous.
\subsubsection{Cropping Images}
-The cropping was performed using a python script extended from an open source image viewer, using a tile system for performance when zooming an panning\footnote{\url{https://github.com/foobar167/junkyard/blob/master/zoom_advanced.py}}. This was extended by adding a parameter for a target directory of images as well as a keybinding to crop and cycle through the images.
+The application for efficiently cropping the images manually was built ontop of a zooming-application which utilizes tiles for increased performance. The frame border of the application window was set to a 1:1 aspect ratio with desired dimensions and could then easily be used to crop every image from a specified input directory, and put the cropped images in a separate (or in the same) destination directory.
+% Add git link
+% Add screenshot
\subsection{Label App: Hentai Tinder} \label{sec:impl_labelapp}
-TODO: Update this section!!\\
The name of the label application is ``Hentai Tinder''\\(cred. Hood Classic\#8866).
+% Add screenshot
+% Add git link
\begin{itemize}
\item Tkinter is a Python binding to the Tk GUI toolkit\footnote{\url{https://docs.python.org/3/library/tkinter.html}}
\item Load in batches of 10\%
\item Smash, Pass, Go Back, Save
\item Output file structure
- \item Resize to 250x250px
\end{itemize}
+The output of the Hentai Tinder application is a csv file which can be easily used in fastai to create a dataloader with all the images including their labels.
+% Include the head of a sample csv file
+
+\subsection{Deep Learning with fastai} \label{sec:impl_deeplearning}
+% How was fastai implemented
+% Add git link
-\subsection{Deep Learning with fast.ai} \label{sec:impl_deeplearning}
+\section{Results} \label{sec:results}
+% Two more csv files
+% Cool graphs of AI performance
\section{Discussion} \label{sec:discussion}
\subsection{Limitations} \label{sec:limitations}
+The size of the lewd anime thighs dataset is only 1000 images.
+The small dataset is due to the time-consuming task of manually crop and label the dataset.
\subsection{Future Work} \label{sec:futurework}
+In order to increase the size of the dataset and thereby obtaining a more robust accuracy from the machine learning model, future research in Project Hentai AI will give more thought to the collection, transformation and labeling of data.
\section{Conclusion}
projects / papers / wAiFu.git / .git / commitdiff