PRE2019 3 Group10
Group Members
| Name | Student Number | Study | |
|---|---|---|---|
| Joris Goddijn | 1244648 | Computer Science | j.d.goddijn@student.tue.nl |
| Daniël Verloop | 1263544 | Computer Science | a.c.verloop@student.tue.nl |
| Ka Yip Fung | 1245300 | Computer Science | k.y.fung@student.tue.nl |
| Rik Maas | 1244503 | Computer Science | r.maas@student.tue.nl |
| Jeroen Struijk | 1252070 | Computer Science | j.j.struijk@student.tue.nl |
Problem Statement
Neural Networks have been used extensively in a wide variety of fields. In health care, neural networks are used for detecting certain diseases, for example tumor detection. In businesses, neural networks are used to filter job applicants: this way the business only has to take the best applicants in for interviews. In self driving cars neural networks are used to parse the visual input of the sensors of the car.
A big issue of modern neural networks is that it is a black box approach. Experts do not understand what is going on inside the neural network. This is an issue when people use neural networks to make decisions with real impactful consequences.
Our aim is to provide a tool which our users can use to have a better understanding of how the neural network they are using is making decisions. We are going to make a web application in which users can see visualizations of the inner workings of a neural network. We will focus on showing visualizations of the individual layers of a neural network. The goal of our web application is to help users understand how their neural network is making decisions.
Objectives
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Users and what they need
The users are people who make use of Neural Networks, e.g. people in the health care or students.
These users have need of an environment in which the inner workings of a neural network is visualized in such a way that these inner workings become more understandable.
It also needs to be understandable for people who do not have much pre-knowledge about neural networks or machine learning. This explanation will be a combination of some kind of model and pictures together with text that explains what is happening. Furthermore, users need to be able to explore the model freely, in such a way that they can discover different aspects of the models with various inputs.
Approach
We will use a website where we can visualize the activation of the layers in the neural network by uploading a picture/file. Based on the neural net, the website runs a forward pass on a trained network. This yields a layer activation which is returned to the user to gain information on how the neural net detect these objects.
Milestones
- Week 3: Have a working website with at least 1 visualization.
- Week 7: Finish the project.
Deliverables
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State of the Art
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Task Division
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References
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