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![Fig. 1: cGAN Architecture in Pix2Pix [3]](profile/Dirk-Hoelscher-2/publication/377285055/figure/fig1/AS:11431281234114034@1712221141850/cGAN-Architecture-in-Pix2Pix-3_Q320.jpg)
Generative models and their possible applications are almost limitless. But there are still problems that such models have. On one hand, the models are difficult to train. Stability in training, mode collapse or non convergence, together with the huge parameter space make it extremely costly and difficult to train and optimize generative models. Th...
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... achieved results after 5 epochs were sufficient enough and achieved in a justifiable timespan of 5 minutes. As shown in Figure 2 the lowermost line is the trained baseline with standard parameters whereas two of the three others are well performing parameter sets highlighted in Figure 4 The algorithm works as follows and in addition is shown as a flow chart in Figure3: ...
Citations
... This data was used to train a Pix2Pix GAN which was trained to create exact copies [10] of the scans. Based on our previous work [11], [12] and [13] we optimised Pix2Pix to create high quality samples using the evaluation of the Universal Quality Index Metric (UIQ) [14] to optimise the generated images towards an ideal UIQ score by using hyperparameter tuning and then evaluating the results using UIQ. In addition, we created, based on our experiments a prediction network which is able to predict if a hyperparameter combination is able to generate better results or not. ...
