Unsupervised Discovery, Control, and Disentanglement of Semantic Attributes with Applications to Anomaly Detection

Understand

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.