项目作者: GioStamoulos

项目描述 :
Fashion Mnist and "recognize a speaker" datasets were utilized for image classification. For this classification task were tried to apply transfer learning from Mnist Fashion to "Recognize a Speaker" and transfer learning inside of Mnist Fashion.
高级语言: Jupyter Notebook
项目地址: git://github.com/GioStamoulos/Transfer_Learning_CNN.git
创建时间: 2021-07-11T11:55:18Z
项目社区:https://github.com/GioStamoulos/Transfer_Learning_CNN

开源协议:MIT License

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Transfer_Learning_CNN

About

Fashion Mnist and “recognize a speaker” datasets were utilized for image classification. For this classification task were tried to apply transfer learning from Mnist Fashion to “Recognize a Speaker” and transfer learning inside of Mnist Fashion. Two deep convolutional neural networks architectures, namely simple (Figure 1) and complex (Figure 2).





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  1. Figure 1: Complex CNN architecture.






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  1. Figure 2: Simple CNN architecture.

Mnist Fashion

Fashion-MNIST is a dataset of Zalando’s article images consisting of a training set of 60,000 examples and a test set of 10,000 examples. Each example is a 28x28 grayscale image, associated with a label from 10 classes.
Labels = [‘T-shirt’, ‘Trouser’, ‘Pullover’, ‘Dress’, ‘Coat’, ‘Sandal’, ‘Shirt’, ‘Sneaker’, ‘Bag’ & ‘Ankle boot’]






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  1. Figure 3: Mnist Fashion samples.

Recognize a speaker

Dataset that contains wav rec file from 5 different speakers. From the purpose of this project every wav sample were converted to melspectrogram (red scale).
Labels = [‘Benjamin_Netanyau’, ‘Jens_Stoltenberg’, ‘Julia_Gillard’, ‘Magaret_Tarcher’ & ‘Nelson_Mandela΄]

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  1. Figure 4: Recognize a speaker samples.

Transfer Learning

The two datasets were utilized for image classification. For this classification task were tried to apply transfer learning from Mnist Fashion to “Recognize a Speaker” and transfer learning inside of Mnist Fashion. Transfer learning (TL) is a research problem in machine learning (ML) that focuses on storing knowledge gained while solving one problem and applying it to a different but related problem [West et.al. 2007]. Were utilized the 2 different CNN architectures for the same transfer learning experiments just for comparing.

Transfer Learning tasks

• Train Under5 labels Fashion Mnist.
• Pretrained (Under5 labels Fashion Mnist) & Train Over5 labels Fashion Mnist.
• Pretrained (Under5 & Over5 labels Fashion Mnist) & Train all labels Fashion Mnist.
• Pretrained (Under5 labels Fashion Mnist) & Train all labels Fashion Mnist.
• Pretrained (Under5 labels Fashion Mnist) & Train “Recognize a Speaker”.
• Pretrained (Under5 & Over5 labels Fashion Mnist) & Train “Recognize a Speaker”.
• Pretrained (Under5 & all labels Fashion Mnist) & Train “Recognize a Speaker”.