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This thesis presents a system, which builds on the Generative Adversarial Network (GAN) framework, with the focus of learning interpretable representations of data. The system is able to learn representations of data that are ordered in regards to the saliency of the attributes, in a completely unsupervised manner. The training strategy expands the latent space dimension while appropriately adding
