Tasarımda iğrençlik kavramının üretken çekişmeli ağ algoritması ile değerlendirilmesi
Evaluation of the concept of disgust in design with generative adversarial network algorithm
Yazar: EFECAN SOYSAL
Danışman: DR. ÖĞR. ÜYESİ ETHEM GÜRER
İstanbul Teknik Üniversitesi / Lisansüstü Eğitim Enstitüsü / Bilişim Ana Bilim Dalı / Mimari Tasarımda Bilişim Bilim Dalı
Derece: Yüksek Lisans, 2022
Konu: Mimarlık = Architecture

Özet:
Güzellik ve çirkinlik kavramları kültüre, coğrafyaya, zamana bağlı olarak değişebilen
öznel olgulardır (Eco, 2009). Güzel olan ve çirkin olan birbirleri yerine geçebilmekte
ve değişebilmektedir. Bu değişimlerin izlerini sanat akımlarından ve modadan takip
edebiliriz (Rosenkranz, 2018). Güzellik ve çirkinlik kavramları öznel ve değişken
olduğu için, güzelliğin ve çirkinliğin parametrelerini çözümlemek zorlaşmaktadır.
Güzelliğin ve çirkinliğin parametrelerini net olarak çözümleyemediğimiz için sanat ve
tasarım üretim süreçleri de sancılı olmaktadır. Çalışma, "iğrençlik" kavramının insan
nezdindeki algısal parametrelerini şeffaflaştırmayı ve bu parametreleri üretken
çekişmeli ağ algoritması ile dönüştürerek tasarımda kontrollü bir iğrençlik morfolojisi
üretmeyi hedeflemektedir.
Güzellik ve çirkinlik kavramlarının eş anlamlılarını incelediğimizde karşımıza
çirkinlikle eş anlamlı iğrenç kavramı çıkar (Eco, 2009). İğrenme, iğrenç bir nesne ile
ortaya çıkan bir duygudur ve tüm insanlarda ortak bir savunma mekanizması olarak
görev alır (Rozin, 1986). Güzellik, çirkinlik, iyilik, kötülük gibi kavramlar öznelken,
iğrenme kavramı türsel ortak bir duygudur. İğrenmenin parametreleri ve iğrenmeyi
ortaya çıkaran durumlar ve nesneler bellidir. Yani iğrenme duygusundan yola çıkarak
iğrenç olanı tanımlayabiliriz. İğrençlik, çirkinlikle ilişkilendirildiği için iğrençlikten
çirkine ve çirkinliğin zıttı olan güzellik kavramlarına ulaşılabilir, çirkinliğin ve
güzelliğin parametreleri dolaylı olarak şeffaflaştırılabilir.
Araştırmada iğrenme duygusunu çözümlemek için iğrenme ile ilgili kavramların ve
iğrenme nesnelerinin tespiti yapılmıştır. İğrenme, tür içerisinde ortak ve ana
duygulardan birisi olduğundan (Darwin, 2018), bu duyguyu ortaya çıkaran durumların
da benzer olması beklenmektedir. İğrenme duygusu, koklama ve tatma duyuları başta
olmak üzere görme duyusu ile de ilişkilidir (Kolnai, 2003). İğrenme ile doğrudan
ilişkili olan koklama ve tatma duyuları çalışmanın kapsamında yer almamaktadırlar.
Çalışma, görme duyusuna ve dolaylı olarak koklama ve tatma duyularına etki edebilen
görsel uyaranlar üzerinden incelenmiştir.
Çalışma kapsamında geniş katılımcılı, uluslararası bir anket kurgulanmıştır. Anket ile
uluslararası bir iğrençlik araştırması yapılıp kolektif bir iğrençlik verisi elde edilmiştir.
Bu iğrençlik verisi farklı kültürlerden katılımcıların iğrendikleri iğrenme nesnelerinin
bilgilerini içerir. Katılımcılardan toplanan veri ile iğrenme nesneleri nicel olarak
değerlendirilmiştir. İğrenme nesnelerinin görsel temsilleri biriktirilmiş ve bu görsel
temsilleri doğrudan kullanmak yerine Çekişmeli Üretken Ağ Algoritmaları ile üretken
bir sistemde sahte görseller üretilmiştir. Ham görsel verinin kendisinin kullanılmayıp
üretken bir sistemden üretilen sahte verinin kullanılmasının sebebi, iğrenme
nesnelerinin sahip oldukları morfolojik verinin çözümlenip yeniden üretme amacını taşımaktadır. Üretilen 2 boyutlu sahte görsel veri Houdini yazılımı içerisinde yazılan
kod sayesinde 3 boyutlu hale getirilip kolektif bir iğrenme nesnesi haline getirilmiştir.
Çalışma iğrençlik kavramının tespiti üzerine yoğunlaşır ve onu bilişsel bir zemine
oturtmayı amaçlar. İğrençliği ortaya çıkaran iğrenme nesnelerinin görsel temsillerinin
makine öğrenmesinin alt konusu olan Çekişmeli Üretken Ağ Algoritmaları ile
işleyerek sürekli olarak iğrenme duygusunu tetikleyebilecek sahte görseller ve 3
boyutlu tasarımlar üretilmiştir. Bu sayede üretken bir sistemden iğrenme algısına etki
edebilecek tasarımlar türeten bir model ortaya çıkartılması amaçlanmaktadır.

Abstract:
The concepts of beauty and ugliness are subjective phenomena that can change
depending on culture, geography and time (Eco, 2009). The beautiful and the ugly are
interchangeable and interchangeable. We can follow the traces of these changes from
art movements and fashion (Rosenkranz, 2018). Since the concepts of beauty and
ugliness are subjective and variable, it becomes difficult to analyze the parameters of
beauty and ugliness. Since we cannot clearly analyze the parameters of beauty and
ugliness, art and design production processes are also painful. When we examine the
synonyms of the concepts of beauty and ugliness, we encounter the concept of disgust,
which is synonymous with ugliness (Eco, 2009). Disgust is an emotion that arises with
a disgusting object and acts as a common defense mechanism in all people (Rozin,
1986). While concepts such as beauty, ugliness, goodness and evil are subjective, the
concept of disgust is a common generic emotion. The parameters of disgust and the
situations and objects that elicit disgust are clear. In this case, we can define the
disgusting based on the feeling of disgust. Since ugliness is associated with ugliness,
concepts of beauty from ugliness to ugliness and the opposite of ugliness can be
reached, and the parameters of ugliness and beauty can be indirectly made transparent.
The study aims to make the perceptual parameters of the concept of "disgusting"
transparent and to produce a controlled morphology of disgust in the design by
transforming these parameters with a generative adversarial network algorithm. In the
context of producing the morphology of disgust, the concept of "collective disgust
object" has been put forward, which can affect the perception of disgust by all people.
The collective object of disgust is something that all people would find disgusting to
a certain degree. It is important to detect people's perceptions of disgust for the
production of the object of collective disgust. People's perception of disgust was
determined by the surveys to be carried out within the scope of the study.
In order to analyze the feeling of disgust in the research, the concepts and objects of
disgust were determined. Since disgust is one of the common and main emotions in
the genre (Darwin, 2018), the situations that reveal this emotion are expected to be
similar. The feeling of disgust is also related to the sense of sight, especially the senses
of smell and taste (Kolnai, 2003). The senses of smell and taste, which are directly
related to disgust, are not included in the study. The study was examined through visual
stimuli that can affect the sense of sight and indirectly the senses of smell and taste.
The determination of the perception of disgust was made on the basis of Ferdinand de
Saussure's (1998) Triad of Sign, Signified and Signifier. Although disgust is a common
emotion, the perception of disgust differs in each participant with the environmental
conditions. Two international surveys with large participants were designed to
determine the perception of disgust. Before starting the questionnaires, literature research was conducted and "objects of disgust" were identified that affect the
perception of disgust. In the first questionnaire, Survey A-01, objects of disgust were
listed in writing and participants were asked to score points according to the level of
disgust of objects of disgust. With the first survey, Survey-A01, written indicators
(concepts) were given to the users and it was tried to reach the concepts that the
participants detested, that is, the ones that were shown. With the first survey Survey-
A01, an international disgusting research was conducted and a collective disgusting
data was obtained. This disgust data includes information about objects of disgust that
participants from different cultures are disgusted with. The objects of disgust were
evaluated quantitatively with the data collected from the participants. In the first
question of the second survey, Survey-B01, the participants were asked to draw a
disgusting drawing on an empty space. In the second question of Survey-B01, the
participants were given a drawing of a sphere and asked to turn this drawing into a
disgusting sphere. With Survey-B01, it was tried to reach the signified in the minds of
the participants by producing visual indicators.
It is obligatory to transfer the private data in the minds of the participants, which are
aimed to be collected by surveys, to the general, and to make the data workable and
reproducible. The first tool that comes to mind with the regeneration of data by
induction method is artificial intelligence algorithms. Artificial intelligence algorithms
are a general concept with many sub-titles and artificial intelligence includes machine
learning by definition. Throughout the study, the concept of disgust in design will be
examined through visual data. Processing visual data and producing similar images
from the data is possible with the special artificial neural network architecture of GAN
algorithms.
The results obtained from the questionnaires were evaluated according to their weights
and a disgust data set was created based on the disgust objects to be processed in the
GAN algorithm. The disgust data set consists of real disgust objects and was created
by searching the names of the disgust objects through search engines and collecting
photos that are open to share from websites such as Instagram and Reddit. The data set
created with the disgusting concepts and questionnaires identified in the literature was
transferred to the GAN algorithm and processed. By processing real visual data with
GAN algorithm, a generative model has been developed that produces fake likenesses
of real visual data. Thanks to the fake visual data produced by the GAN algorithm, the
relationship between the indicators and the signified has been diversified. Because the
indicators produced by the GAN algorithm are new and fake, but they still maintain
their formal connection and relationship with the first data.
The necessity of cleaning the raw data set emerged from the experiments of GAN
algorithms, and the parts of the images in the data set that were not related to the object
of disgust were manually cleaned with the help of Adobe Photoshop. By processing
the visual data consisting of disgust objects with StyleGAN2, a GAN algorithm, a
generative model and 2D pseudo-disgust objects were obtained. The GAN algorithm
used is an algorithm that works with 2D data, and making 2D data 3D is another part
of the study. The problem of rendering this 2D visual data into 3D was solved by using
voxel tools in Houdini software.
After the indicators were processed with GAN algorithms, 3 types of data output were
produced: fake visual data, latent space walk videos and 3D disgust object. All these
outputs were planned to be evaluated by the participants with a new questionnaire. In
this way, it was aimed to investigate the correlations of the results of the disgusting perceptions of the participants in the first survey with the collective disgusting objects
produced. As a result, with the GAN algorithm, the boundaries of the sign and the
signified blur and a new signified emerges. The new signifier created with the GAN
algorithm has a relationship with the data set processed by artificial intelligence. This
new concept emerges as a combination of disgusting, a mixture of concepts in the
minds of the participants, and as a result, modeling of disgust becomes possible.
The objects of collective disgust produced by the study have common formal features.
These are organic knuckle, wetness and color properties. This information can be used
in the early stages of design and artwork. The study focuses on the detection of the
concept of disgust and aims to put it on a cognitive basis. By processing the visual
representations of objects of disgust that reveal disgust with Generative Adversarial
Network Algorithms, which is a sub-topic of machine learning, fake images and 3D
designs that can trigger the feeling of disgust are produced continuously. In this way,
it is aimed to create a model that generates designs that can affect the perception of
disgust from a productive system.