LAUSR

In recent years, selecting manipulation of data attributes by changing latent code using auto-encoder has received considerable scholarly attention . However, the representation of the data encoded by the auto-encoder cannot be visually observed. Furthermore, the attribute values and the latent code of the dimension do not conform to a linear monotonic relationship. From a practical point of view, we propose a novel method that uses the encoder–decoder architecture to disentangle data into two visualizable representations that are encoded as latent spaces. Consequently, the encoded latent space can be used to manipulate data attributes in a simple and intuitive way. The experiments on image dataset and music dataset show that the proposed approach leads to produce complete interpretable latent spaces, which can be used to manipulate a wide range of data attributes and to generate realistic music via analogy.