  Learning disentangled representations requires either supervision or the
introduction of specific model designs and learning constraints as biases.
InfoGAN is a popular disentanglement framework that learns unsupervised
disentangled representations by maximising the mutual information between
latent representations and their corresponding generated images. Maximisation
of mutual information is achieved by introducing an auxiliary network and
training with a latent regression loss. In this short exploratory paper, we
study the use of the Hilbert-Schmidt Independence Criterion (HSIC) to
approximate mutual information between latent representation and image, termed
HSIC-InfoGAN. Directly optimising the HSIC loss avoids the need for an
additional auxiliary network. We qualitatively compare the level of
disentanglement in each model, suggest a strategy to tune the hyperparameters
of HSIC-InfoGAN, and discuss the potential of HSIC-InfoGAN for medical
applications.

Computer Vision and Pattern Recognition