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Institute
In no field of computer science has the hardware developed as rapidly as in the field of computer graphics. Today, we can display complex, geometric scenes in real time in immersive systems and also integrate elaborate simulations.
The aim of this work is to realize the simulation of paint splashes in a virtual world. For this purpose, an application will be implemented with the help of Unity, that uses three different techniques to color the environment with the help of paint splashes. Based on this application, the limits and possibilities of the techniques in virtual environments are examined more closely.
This examination shows that an inverse projection produces the best results.
Artificial neural networks is a popular field of research in artificial intelli-
gence. The increasing size and complexity of huge models entail certain
problems. The lack of transparency of the inner workings of a neural net-
work makes it difficult to choose efficient architectures for different tasks.
It proves to be challenging to solve these problems, and with a lack of in-
sightful representations of neural networks, this state of affairs becomes
entrenched. With these difficulties in mind a novel 3D visualization tech-
nique is introduced. Attributes for trained neural networks are estimated
by utilizing established methods from the area of neural network optimiza-
tion. Batch normalization is used with fine-tuning and feature extraction to
estimate the importance of different parts of the neural network. A combi-
nation of the importance values with various methods like edge bundling,
ray tracing, 3D impostor and a special transparency technique results in a
3D model representing a neural network. The validity of the extracted im-
portance estimations is demonstrated and the potential of the developed
visualization is explored.