Representation Learning for Style and Content Disentanglement with Autoencoders
Many approaches have been proposed to disentangle the style and content from image representations. Most existing methods aim to create new images from the combinations of separated style and content. These approaches have shown impressive results in creating new images in such a way as to swap or mix features, but they have not shown the results of disentanglement respectively. In this paper, to show up the results of disentanglement on each, we propose a two branch autoencoder framework. Our proposed framework, which incorporates content branch and style branch, enables complementary and effective end to end learning. To demonstrate our framework in a qualitative assessment, we show the disentanglement results of style and content respectively in handwritten digit datasets: MNIST M and EMNIST M. Furthermore, for the quantitative assessment, we examined our method to the classification task. In this experiment, our disentangled content images from digit datasets can achieve competitive performance in classification tasks. Our code is available at https:/github.com/najaemin92/disentanglement pytorch.
ACPR