Bureaucrats, dave_user, Administrators
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→Data augmentation with image synthesis
In the previous articles, when facing a similar problem of lack of data, data augmentation was employed with ease by simply creating input pipelines with image re-mapping operations, or by using OpenCV functions to increase the amount of data stored in memory, more or less in the same fashion as TensorFlow image APIs.
However, despite being a good and very common methodology to increase model robustness for the classification task, new images are generated directly from an already existent image hence . Thus, newly created images are always based on already existent features, such as shapes, edges, contours , etc. ThereforeFor this reason, it can be really a matter of interest to explore the possibility to generate of generating new data based on features of the entire original subset. This can be achieved through ML techniques applied to artificial image synthesis, in particular with the help of Generative Adversarial Networks (GANs).
===Generative adversarial networksAdversarial Networks===
GANs are a particular typology of generative model used for unsupervised learning, which attempt to synthesize new data that is indistinguishable from the training data i.e. with the same distribution function of original data.
