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Wasserstein Generative Adversarial Privacy Networks

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Figure

Figure 2.1.: A graph of the FFNP privatiser. The number in the parentheses is theamount of neurons in that layer.
Table 2.1.: A table of the results of networks trained for 5,000 epochs with varyingprivatisers and allowed distortions
Figure 2.4.: A graph of the accuracy of the adversary compared to the target alloweddistortion per pixel, using either a FFNP or a TCNNP privatiser
Figure 3.1.: A computation scheme of how an ordinary privatiser GAN as used by [1].
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