🤖 AI Summary
Existing deep multivariate models are typically tailored to specific tasks, resulting in limited generalization capability. This work proposes a universal modeling framework that parameterizes the conditional distribution of each variable given all others using deep neural networks and represents the joint distribution through a Markov chain kernel. The model is trained by maximizing the likelihood under the stationary distribution of this kernel. By design, the approach eliminates the need for task-specific architectural modifications and inherently supports arbitrary downstream tasks as well as diverse semi-supervised learning scenarios. Consequently, it not only enhances model generalization but also significantly improves the efficiency of leveraging unlabeled data.
📝 Abstract
We consider deep multivariate models for heterogeneous collections of random variables. In the context of computer vision, such collections may e.g. consist of images, segmentations, image attributes, and latent variables. When developing such models, most existing works start from an application task and design the model components and their dependencies to meet the needs of the chosen task. This has the disadvantage of limiting the applicability of the resulting model for other downstream tasks. Here, instead, we propose to represent the joint probability distribution by means of conditional probability distributions for each group of variables conditioned on the rest. Such models can then be used for practically any possible downstream task. Their learning can be approached as training a parametrised Markov chain kernel by maximising the data likelihood of its limiting distribution. This has the additional advantage of allowing a wide range of semi-supervised learning scenarios.