  Our work focuses on unsupervised and generative methods that address the
following goals: (a) learning unsupervised generative representations that
discover latent factors controlling image semantic attributes, (b) studying how
this ability to control attributes formally relates to the issue of latent
factor disentanglement, clarifying related but dissimilar concepts that had
been confounded in the past, and (c) developing anomaly detection methods that
leverage representations learned in (a). For (a), we propose a network
architecture that exploits the combination of multiscale generative models with
mutual information (MI) maximization. For (b), we derive an analytical result
(Lemma 1) that brings clarity to two related but distinct concepts: the ability
of generative networks to control semantic attributes of images they generate,
resulting from MI maximization, and the ability to disentangle latent space
representations, obtained via total correlation minimization. More
specifically, we demonstrate that maximizing semantic attribute control
encourages disentanglement of latent factors. Using Lemma 1 and adopting MI in
our loss function, we then show empirically that, for image generation tasks,
the proposed approach exhibits superior performance as measured in the quality
and disentanglement trade space, when compared to other state of the art
methods, with quality assessed via the Frechet Inception Distance (FID), and
disentanglement via mutual information gap. For (c), we design several systems
for anomaly detection exploiting representations learned in (a), and
demonstrate their performance benefits when compared to state-of-the-art
generative and discriminative algorithms. The above contributions in
representation learning have potential applications in addressing other
important problems in computer vision, such as bias and privacy in AI.
