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-Yann LeCun, Yoshua Bengio, and Geoffrey~E. Hinton.
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-\newblock {\em Nat.}, 521(7553):436--444, 2015.
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-\newblock Machine learning.
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-\documentclass[conference]{IEEEtran}
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-\begin{document}
-
-\title{Project Hentai AI: wAiFu\\
-\LARGE Reviewing Anime Thighs with Deep Learning}
-
-\author{\IEEEauthorblockN{1\textsuperscript{st} hentai-ai}
-\IEEEauthorblockA{\textit{Project Hentai AI} \\
-\url{https://www.hentai-ai.org}
-}
-}
-
-\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.
-\end{abstract}
-
-\begin{IEEEkeywords}
-deep learning, DL, machine learning, ML, artificial intelligence, AI, thighs, thighdeology, thicc, lewd, ecchi, hentai
-\end{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).
-
-\section{Background} \label{sec:background}
-
-\subsection{Artificial Intelligence} \label{sec:ai}
-\emph{Artificial Intelligence} (AI) is an umbrella term for the area in computer science aiming to artificially create an intelligent software using statistics and algorithms. There is an important distinction here between Intelligence and Consciousness. An AI which can calculate the best move in chess could be considered intelligent, but does not necessary have a consciousness (a notion of self). The simplest forms of AI are the Non-Playable Characters (NPCs) and bots of video games. The main goal of these AI is to emulate human behavior in order to create an illusion of intelligence and/or consciousness.
-
-\emph{Machine Learning} (ML) is a subset of AI which is best described by Tom M. Mitchell~\cite{machinelearning}:
-\begin{quote}
- \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}).
-
-\begin{figure}[h]
- \centering
- \includegraphics{img/ai_diagram.pdf}
- \caption{Relation between Artificial Intelligence, Machine Learning and Deep Learning.}
- \label{fig:ai}
-\end{figure}
-
-Machine Learning and Deep Learning falls under the discipline of Artificial Intelligence in computer science, visually presented in Figure~\ref{fig:ai}.
-
-\subsection{Hentai and Thighdeology} \label{sec:hentai}
-For the purpose of this study and future studies in Project Hentai AI, the data in the datasets are categorised in three definitions: \emph{Hentai}, \emph{Ecchi} and \emph{Lewd}.
-In its simplest definition, Hentai is anime and manga pornography and can be seen as the highest tier out of the three. Ecchi on the other hand, when used as an adjective, translates to ``sexy'', ``dirty'' or ``naughty'', and has been used to describe anime and manga with \emph{sexual overtones} (playful sexuality or softcore). Lewd in these studies is defined as \emph{sexual undertones}.
-Project Hentai AI includes hentai, ecchi and lewd but groups them together in the name of the project under the term hentai for simplicity.
-% Todo: add images to demonstrate?
-
-Thighdeology is the worship of thick anime thighs which has its Mecca on the Thighdeology subreddit\footnote{\url{https://www.reddit.com/r/thighdeology/}}.
-The top two rules on the subreddit are: (1) All images must be thigh-focused and (2) No Pictures of Sex (Nudity is allowed). The second rule is a clear demonstration of the distinction between hentai and ecchi described above.
-The epigraph which crowns the website says it all:
-\begin{quote}
- \emph{``Blessed is the man that walketh not in the counsel of the ungodly, nor standeth in the way of sinners, nor sitteth in the seat of the scornful. But his delight is in the law of the THICC anime thighs.''}
-\end{quote}
-
-
-
-
-
-\section{Method} \label{sec:method}
-
-\subsection{Data Collection} \label{sec:datacollection}
-\noindent The data was collected manually from six separate sources:
-\begin{itemize}
- \item Discord Server: All Things Hentai
- \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 Private Donations
-\end{itemize}
-
-~\\\noindent After collection, the data was manually screened for (A) presence of thighs (B) image quality and (C) image ``cropability''. The presence of thighs simply implies that the image in question contains a section of the lower body of a humanoid character. The vast majority of the characters depicted in the images collected were of the feminine nature, although this was most likely due to the skewed ratio of feminine/masculine thighs from the sources themselves and not due to any discrimination during the collecting. This is further discussed within limitations in Section~\ref{sec:limitations}.
-
-Image quality refers to the resolution of the picture. When finding duplicates, the one with higher resolution was kept. Some images where included in the dataset even if the quality of the resolution was below average due to either its content or source.
-
-Image cropability refers to the composition of the picture. Since the focus of the first dataset in wAiFu is ``thighs'', it is preferred to isolate the thighs from other factors in the image which could influence the labeling, such as: faces, tits and other eye-catching details (some of the cropped images in the dataset does contain the ass region due to non-perfect but acceptable levels of cropability).
-
-\subsection{Data Transformation} \label{sec:datatransformation}
-The data transformation in this project consist of three stages after being collected:
-\begin{enumerate}
- \item Converting
- \item Renaming
- \item Cropping
-\end{enumerate}
-In order to get a uniform dataset the images collected were converted from JPG/JPEG to PNG.
-The naming convention was arbitrarily decided to be structured as \textbf{thighs-id.png} where
-\textbf{id} is an increasing nonce (number only used once) padded with four zeroes (e.g., \textbf{thighs-0001.png}).
-
-The images were then cropped to contain as little as possible apart from the topic at hand (thighs). This was done with the intention of focusing both the manual labeling process as well as the machine learning training on the thighs. If the character on the image would have a certain hair color this could potentially influence the user when labeling the dataset, and later might be picked up during the learning and thus distorting the focus on the subject matter for this study.
-
-The cropping was performed by leveraging an open source module called \emph{interactivecrop}\footnote{\url{https://openbits.app/posts/python-interactive-cropping/}} installed via pip. A custom callback method was used to save the cropped subsection of the image.
-After cropping the original non-cropped images are kept with their original name, while the newly cropped images get an appended notation of having undergone the procedure (e.g., \textbf{thighs-0001-crop.png}).
-The cropping was done manually by hand, using the interface provided in interactivecrop which resulted in that the cropped images were rectangles approximating squares. The implications of this when training the machine learning model with the dataset is further described in Section~\ref{sec:limitations}.
-
-The cropped images were placed in a separate directory from the original images. By keeping both datasets, this study provides the possibility of utilizing the non-cropped images for future work.
-
-\subsection{Data Labeling} \label{sec:datalabeling}
-The labeling of the data is categorised in three 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.
-% Todo: add model
-
-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).
-
-\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}
-% Talk about the overview of the framework, the main idea
-
-\section{Implementation} \label{sec:implementation}
-All code is open source and can be found on GitHub\footnote{\url{https://github.com/hentai-ai}}
-
-\subsection{Data Transformations} \label{sec:datatfms}
-\begin{itemize}
- \item Converting to PNG
- \item Renaming
- \item Cropping with interactivecrop
-\end{itemize}
-
-\subsection{Label App: Hentai Tinder} \label{sec:impl_labelapp}
-The name of the label application is ``Hentai Tinder''\\(cred. Hood Classic\#8866).
-\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}
-
-\subsection{Deep Learning with fast.ai} \label{sec:impl_deeplearning}
-
-\section{Discussion} \label{sec:discussion}
-
-\subsection{Limitations} \label{sec:limitations}
-
-\subsection{Future Work} \label{sec:futurework}
-
-\section{Conclusion}
-
-\bibliography{ref}
-\bibliographystyle{plain}
-
-\end{document}
--- /dev/null
+\relax
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--- /dev/null
+\begin{thebibliography}{1}
+
+\bibitem{fastai}
+Jeremy Howard and Sylvain Gugger.
+\newblock Fastai: {A} layered {API} for deep learning.
+\newblock {\em Inf.}, 11(2):108, 2020.
+
+\bibitem{deeplearning}
+Yann LeCun, Yoshua Bengio, and Geoffrey~E. Hinton.
+\newblock Deep learning.
+\newblock {\em Nat.}, 521(7553):436--444, 2015.
+
+\bibitem{machinelearning}
+Tom~M Mitchell et~al.
+\newblock Machine learning.
+\newblock 1997.
+
+\end{thebibliography}
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--- /dev/null
+\documentclass[conference]{IEEEtran}
+\IEEEoverridecommandlockouts
+\usepackage{cite}
+\usepackage{amsmath,amssymb,amsfonts}
+\usepackage{graphicx}
+\usepackage{textcomp}
+\usepackage{xcolor}
+\usepackage{url}
+\def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em
+ T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}}
+\begin{document}
+
+\title{Project Hentai AI: wAiFu\\
+\LARGE Reviewing Anime Thighs with Deep Learning}
+
+\author{\IEEEauthorblockN{1\textsuperscript{st} hentai-ai}
+\IEEEauthorblockA{\textit{Project Hentai AI} \\
+\url{https://www.hentai-ai.org}
+}
+}
+
+\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.
+\end{abstract}
+
+\begin{IEEEkeywords}
+deep learning, DL, machine learning, ML, artificial intelligence, AI, thighs, thighdeology, thicc, lewd, ecchi, hentai
+\end{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).
+
+\section{Background} \label{sec:background}
+
+\subsection{Artificial Intelligence} \label{sec:ai}
+\emph{Artificial Intelligence} (AI) is an umbrella term for the area in computer science aiming to artificially create an intelligent software using statistics and algorithms. There is an important distinction here between Intelligence and Consciousness. An AI which can calculate the best move in chess could be considered intelligent, but does not necessary have a consciousness (a notion of self). The simplest forms of AI are the Non-Playable Characters (NPCs) and bots of video games. The main goal of these AI is to emulate human behavior in order to create an illusion of intelligence and/or consciousness.
+
+\emph{Machine Learning} (ML) is a subset of AI which is best described by Tom M. Mitchell~\cite{machinelearning}:
+\begin{quote}
+ \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}).
+
+\begin{figure}[h]
+ \centering
+ \includegraphics{img/ai_diagram.pdf}
+ \caption{Relation between Artificial Intelligence, Machine Learning and Deep Learning.}
+ \label{fig:ai}
+\end{figure}
+
+Machine Learning and Deep Learning falls under the discipline of Artificial Intelligence in computer science, visually presented in Figure~\ref{fig:ai}.
+
+\subsection{Hentai and Thighdeology} \label{sec:hentai}
+For the purpose of this study and future studies in Project Hentai AI, the data in the datasets are categorised in three definitions: \emph{Hentai}, \emph{Ecchi} and \emph{Lewd}.
+In its simplest definition, Hentai is anime and manga pornography and can be seen as the highest tier out of the three. Ecchi on the other hand, when used as an adjective, translates to ``sexy'', ``dirty'' or ``naughty'', and has been used to describe anime and manga with \emph{sexual overtones} (playful sexuality or softcore). Lewd in these studies is defined as \emph{sexual undertones}.
+Project Hentai AI includes hentai, ecchi and lewd but groups them together in the name of the project under the term hentai for simplicity.
+% Todo: add images to demonstrate?
+
+Thighdeology is the worship of thick anime thighs which has its Mecca on the Thighdeology subreddit\footnote{\url{https://www.reddit.com/r/thighdeology/}}.
+The top two rules on the subreddit are: (1) All images must be thigh-focused and (2) No Pictures of Sex (Nudity is allowed). The second rule is a clear demonstration of the distinction between hentai and ecchi described above.
+The epigraph which crowns the website says it all:
+\begin{quote}
+ \emph{``Blessed is the man that walketh not in the counsel of the ungodly, nor standeth in the way of sinners, nor sitteth in the seat of the scornful. But his delight is in the law of the THICC anime thighs.''}
+\end{quote}
+
+
+
+
+
+\section{Method} \label{sec:method}
+
+\subsection{Data Collection} \label{sec:datacollection}
+\noindent The data was collected manually from six separate sources:
+\begin{itemize}
+ \item Discord Server: All Things Hentai
+ \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 Private Donations
+\end{itemize}
+
+~\\\noindent After collection, the data was manually screened for (A) presence of thighs (B) image quality and (C) image ``cropability''. The presence of thighs simply implies that the image in question contains a section of the lower body of a humanoid character. The vast majority of the characters depicted in the images collected were of the feminine nature, although this was most likely due to the skewed ratio of feminine/masculine thighs from the sources themselves and not due to any discrimination during the collecting. This is further discussed within limitations in Section~\ref{sec:limitations}.
+
+Image quality refers to the resolution of the picture. When finding duplicates, the one with higher resolution was kept. Some images where included in the dataset even if the quality of the resolution was below average due to either its content or source.
+
+Image cropability refers to the composition of the picture. Since the focus of the first dataset in wAiFu is ``thighs'', it is preferred to isolate the thighs from other factors in the image which could influence the labeling, such as: faces, tits and other eye-catching details (some of the cropped images in the dataset does contain the ass region due to non-perfect but acceptable levels of cropability).
+
+\subsection{Data Transformation} \label{sec:datatransformation}
+The data transformation in this project consist of three stages after being collected:
+\begin{enumerate}
+ \item Converting
+ \item Renaming
+ \item Cropping
+\end{enumerate}
+In order to get a uniform dataset the images collected were converted from JPG/JPEG to PNG.
+The naming convention was arbitrarily decided to be structured as \textbf{thighs-id.png} where
+\textbf{id} is an increasing nonce (number only used once) padded with four zeroes (e.g., \textbf{thighs-0001.png}).
+
+The images were then cropped to contain as little as possible apart from the topic at hand (thighs). This was done with the intention of focusing both the manual labeling process as well as the machine learning training on the thighs. If the character on the image would have a certain hair color this could potentially influence the user when labeling the dataset, and later might be picked up during the learning and thus distorting the focus on the subject matter for this study.
+
+The cropping was performed by leveraging an open source module called \emph{interactivecrop}\footnote{\url{https://openbits.app/posts/python-interactive-cropping/}} installed via pip. A custom callback method was used to save the cropped subsection of the image.
+After cropping the original non-cropped images are kept with their original name, while the newly cropped images get an appended notation of having undergone the procedure (e.g., \textbf{thighs-0001-crop.png}).
+The cropping was done manually by hand, using the interface provided in interactivecrop which resulted in that the cropped images were rectangles approximating squares. The implications of this when training the machine learning model with the dataset is further described in Section~\ref{sec:limitations}.
+
+The cropped images were placed in a separate directory from the original images. By keeping both datasets, this study provides the possibility of utilizing the non-cropped images for future work.
+
+\subsection{Data Labeling} \label{sec:datalabeling}
+The labeling of the data is categorised in three 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.
+% Todo: add model
+
+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).
+
+\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}
+% Talk about the overview of the framework, the main idea
+
+\section{Implementation} \label{sec:implementation}
+All code is open source and can be found on GitHub\footnote{\url{https://github.com/hentai-ai}}
+
+\subsection{Data Transformations} \label{sec:datatfms}
+\begin{itemize}
+ \item Converting to PNG
+ \item Renaming
+ \item Cropping with interactivecrop
+\end{itemize}
+
+\subsection{Label App: Hentai Tinder} \label{sec:impl_labelapp}
+The name of the label application is ``Hentai Tinder''\\(cred. Hood Classic\#8866).
+\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}
+
+\subsection{Deep Learning with fast.ai} \label{sec:impl_deeplearning}
+
+\section{Discussion} \label{sec:discussion}
+
+\subsection{Limitations} \label{sec:limitations}
+
+\subsection{Future Work} \label{sec:futurework}
+
+\section{Conclusion}
+
+\bibliography{ref}
+\bibliographystyle{plain}
+
+\end{document}
projects / papers / wAiFu.git / .git / commitdiff