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Figure 5. Option C. VGM model including the labels in the decoder with Gaussian and Bernoulli distributions. Training phase. The process of training and data generation is different for this model when compared with previous models. In previous models, both the training and generation phases are done with the labels as input and the feature vectors as outputs, using the encoder and decoder blocks for the two phases. For this model, after the training phase is done, we will employ only the decoder block of the trained model to generate new samples (Figure 6). To achieve that, we will provide two inputs to the decoder block: a vector of selected labels and a vector of random values sampled from a standard normal distribution with zero mean and unit variance. The output of the decoder block will be the desired generated samples. These samples are constructed using the probability distributions of the features associated to the labels given as input. All the models presented, for all options, have the same objective: to be able to generate new data relying exclusively on the labels at the generation phase. This objective is also maintained in this option, because at generation phase we need only the labels and a vector of standard normal random values which are independent of everything else. Figure 6 Option C. VGM model including the labels in the decoder with Gaussian and Bernoulli distributions. Generation phase. Doctoral Thesis: Novel applications of Machine Learning to NTAP - 171PDF Image | Novel applications of Machine Learning to Network Traffic Analysis
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