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@@ -35,11 +35,11 @@ We implemented this assignment using mainly Keras and Sklearn.
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  An example for the ‘Adults’ dataset:
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- ![](https://github.com/nevoit/Generative-Adversarial-Network-/blob/master/figures/Aspose.Words.36be2542-1776-4b1c-8010-360ae82480ae.001.png)
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  An example for the ‘Bank-full’ dataset:
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- ![](https://github.com/nevoit/Generative-Adversarial-Network-/blob/master/figures/Aspose.Words.36be2542-1776-4b1c-8010-360ae82480ae.002.png)
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  **Code Design:**
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@@ -150,11 +150,11 @@ For adults dataset, the results of the model were:
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  - MMDNF = Mean minimum euclidean distance for the not fooled samples was 0.422
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  - Several samples that “fooled” the detector:
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- ![](https://github.com/nevoit/Generative-Adversarial-Network-/blob/master/figures/Aspose.Words.36be2542-1776-4b1c-8010-360ae82480ae.003.png)
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  - Several samples that “not fooled” the detector:
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- ![](https://github.com/nevoit/Generative-Adversarial-Network-/blob/master/figures/Aspose.Words.36be2542-1776-4b1c-8010-360ae82480ae.004.png)
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  - Plotting the PCA shows that the fooled samples are very similar to the real data and the not fooled samples are less similar.
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  An example for the ‘Adults’ dataset:
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+ ![](https://huggingface.co/nevoit//Generative-Adversarial-Network-/blob/master/figures/Aspose.Words.36be2542-1776-4b1c-8010-360ae82480ae.001.png)
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  An example for the ‘Bank-full’ dataset:
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+ ![](https://huggingface.co/nevoit/Generative-Adversarial-Network-/blob/master/figures/Aspose.Words.36be2542-1776-4b1c-8010-360ae82480ae.002.png)
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  **Code Design:**
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  - MMDNF = Mean minimum euclidean distance for the not fooled samples was 0.422
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  - Several samples that “fooled” the detector:
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+ ![](huggingface.co/nevoit/Generative-Adversarial-Network-/blob/master/figures/Aspose.Words.36be2542-1776-4b1c-8010-360ae82480ae.003.png)
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  - Several samples that “not fooled” the detector:
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+ ![](huggingface.co/nevoit/Generative-Adversarial-Network-/blob/master/figures/Aspose.Words.36be2542-1776-4b1c-8010-360ae82480ae.004.png)
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  - Plotting the PCA shows that the fooled samples are very similar to the real data and the not fooled samples are less similar.
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