Improving the robustness of machine learning system through convolutional neural network
This report is to study what is a Convolutional Neural Network and carry out a multi-layer network surgery on the AlexNet Convolutional Neural Network. The AlexNet network consists of five convolutional layers and three fully connected layers. Noise error layers are then created to fit into the vari...
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| Format: | Final Year Project (FYP) |
| Language: | English |
| Published: |
2019
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| Online Access: | http://hdl.handle.net/10356/77810 |
| _version_ | 1826110844849618944 |
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| author | Chia, Daryl Jing |
| author2 | Chang Chip Hong |
| author_facet | Chang Chip Hong Chia, Daryl Jing |
| author_sort | Chia, Daryl Jing |
| collection | NTU |
| description | This report is to study what is a Convolutional Neural Network and carry out a multi-layer network surgery on the AlexNet Convolutional Neural Network. The AlexNet network consists of five convolutional layers and three fully connected layers. Noise error layers are then created to fit into the various layers. These error layers with number of noise element being roughly 10% of total elements per layer are then injected into the network to see how the output will be affected. This is to find out how robust each layer towards error. This report will cover the methods and steps that I took to carry out the surgery, create the error layers of the correct size and injecting of the errors into the network. I will break down the codes that I used to achieve the above chunk by chunk in the Methodology Chapter. Thereafter, I will be discussing the results and findings of each testing in each individual layer of the network. The report will end with a conclusion and recommended future works. |
| first_indexed | 2024-10-01T02:41:09Z |
| format | Final Year Project (FYP) |
| id | ntu-10356/77810 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T02:41:09Z |
| publishDate | 2019 |
| record_format | dspace |
| spelling | ntu-10356/778102023-07-07T17:16:28Z Improving the robustness of machine learning system through convolutional neural network Chia, Daryl Jing Chang Chip Hong School of Electrical and Electronic Engineering DRNTU::Engineering::Electrical and electronic engineering This report is to study what is a Convolutional Neural Network and carry out a multi-layer network surgery on the AlexNet Convolutional Neural Network. The AlexNet network consists of five convolutional layers and three fully connected layers. Noise error layers are then created to fit into the various layers. These error layers with number of noise element being roughly 10% of total elements per layer are then injected into the network to see how the output will be affected. This is to find out how robust each layer towards error. This report will cover the methods and steps that I took to carry out the surgery, create the error layers of the correct size and injecting of the errors into the network. I will break down the codes that I used to achieve the above chunk by chunk in the Methodology Chapter. Thereafter, I will be discussing the results and findings of each testing in each individual layer of the network. The report will end with a conclusion and recommended future works. Bachelor of Engineering (Electrical and Electronic Engineering) 2019-06-06T08:18:37Z 2019-06-06T08:18:37Z 2019 Final Year Project (FYP) http://hdl.handle.net/10356/77810 en Nanyang Technological University 80 p. application/pdf |
| spellingShingle | DRNTU::Engineering::Electrical and electronic engineering Chia, Daryl Jing Improving the robustness of machine learning system through convolutional neural network |
| title | Improving the robustness of machine learning system through convolutional neural network |
| title_full | Improving the robustness of machine learning system through convolutional neural network |
| title_fullStr | Improving the robustness of machine learning system through convolutional neural network |
| title_full_unstemmed | Improving the robustness of machine learning system through convolutional neural network |
| title_short | Improving the robustness of machine learning system through convolutional neural network |
| title_sort | improving the robustness of machine learning system through convolutional neural network |
| topic | DRNTU::Engineering::Electrical and electronic engineering |
| url | http://hdl.handle.net/10356/77810 |
| work_keys_str_mv | AT chiadaryljing improvingtherobustnessofmachinelearningsystemthroughconvolutionalneuralnetwork |