This paper proposes a novel method for factorising the information in the latent space of an autoencoder (AE), to improve the interpretability of the latent space and facilitate controlled generation. When trained on a dataset with labelled attributes we can produce a latent vector which separates information encoding the attributes from other characteristic information, and also disentangles the attribute information. This then allows us to manipulate each attribute of the latent representation individually without affecting others. Our method, matrix subspace projection, is simpler than the state of the art adversarial network approaches to latent space factorisation. We demonstrate the utility of the method for attribute manipulation tasks on the CelebA image dataset and the E2E text corpus.
@cite_18: We present a method for training variational autoencoders on labelled datasets which encode information corresponding to the labels in explicitly predetermined subspaces of the latent space. We motivate our model from both an information theoretic perspective as well as a adversarial game perspective. By separating labelled information into a less complicated space we allow the model to more easily disentangle representations. This provides a form of semi-supervised learning of attributes. Since these subspaces can be chosen a priori, setting them to be low-dimensional provides a form of dimensionality reduction. We demonstrate the utility of our model on attribute manipulation tasks with several image datasets.
@cite_11: Generative models, such as variational auto-encoders (VAE) and generative adversarial networks (GAN), have been immensely successful in approximating image statistics in computer vision. VAEs are useful for unsupervised feature learning, while GANs alleviate supervision by penalizing inaccurate samples using an adversarial game. In order to utilize benefits of these two approaches, we combine the VAE under an adversarial setup with auxiliary label information. We show that factorizing the latent space to separate the information needed for reconstruction (a continuous space) from the information needed for image attribute classification (a discrete space), enables the capability to edit specific attributes of an image.
While this adversarial approach can successfully remove label information from @math , there is nothing to stop the decoder (generator) from associating other spurious information with the label. For example the decoder might associate the label intended to be for glasses' with an older or more masculine face. This is what we see in their results of two of the adversarial approaches (Figure. ). These examples have been reproduced from the cited papers @cite_11 and @cite_18 , where they are used to showcase how well those systems can independently manipulate attributes. Therefore we assume they are typical examples of the level of disentanglement achieved, and not unrepresentative examples.