  This paper addresses two crucial problems of learning disentangled image
representations, namely controlling the degree of disentanglement during image
editing, and balancing the disentanglement strength and the reconstruction
quality. To encourage disentanglement, we devise a distance covariance based
decorrelation regularization. Further, for the reconstruction step, our model
leverages a soft target representation combined with the latent image code. By
exploring the real-valued space of the soft target representation, we are able
to synthesize novel images with the designated properties. To improve the
perceptual quality of images generated by autoencoder (AE)-based models, we
extend the encoder-decoder architecture with the generative adversarial network
(GAN) by collapsing the AE decoder and the GAN generator into one. We also
design a classification based protocol to quantitatively evaluate the
disentanglement strength of our model. Experimental results showcase the
benefits of the proposed model.
