Flexible and accurate inference and learning for deep generative models

We introduce a new approach to learning in hierarchical latent-variable generative models called the "distributed distributional code Helmholtz machine", which emphasises flexibility and accuracy in the inferential process. In common with the original Helmholtz machine and later variational autoencoder algorithms (but unlike adverserial methods) our approach learns an explicit inference or "recognition" model to approximate the posterior distribution over the latent variables. Unlike in these earlier methods, the posterior representation is not limited to a narrow tractable parameterised form (nor is it represented by samples). To train the generative and recognition models we develop an extended wake-sleep algorithm inspired by the original Helmholtz Machine. This makes it possible to learn hierarchical latent models with both discrete and continuous variables, where an accurate posterior representation is essential. We demonstrate that the new algorithm outperforms current state-of-the-art methods on synthetic, natural image patch and the MNIST data sets

Synthetic Document Generator for Annotation-free Layout Recognition

Analyzing the layout of a document to identify headers, sections, tables, figures etc. is critical to understanding its content. Deep learning based approaches for detecting the layout structure of document images have been promising. However, these methods require a large number of annotated examples during training, which are both expensive and time consuming to obtain. We describe here a synthetic document generator that automatically produces realistic documents with labels for spatial positions, extents and categories of the layout elements. The proposed generative process treats every physical component of a document as a random variable and models their intrinsic dependencies using a Bayesian Network graph. Our hierarchical formulation using stochastic templates allow parameter sharing between documents for retaining broad themes and yet the distributional characteristics produces visually unique samples, thereby capturing complex and diverse layouts. We empirically illustrate that a deep layout detection model trained purely on the synthetic documents can match the performance of a model that uses real documents

A Novel Variational Lower Bound for Inverse Reinforcement Learning

Inverse reinforcement learning (IRL) seeks to learn the reward function from expert trajectories, to understand the task for imitation or collaboration thereby removing the need for manual reward engineering. However, IRL in the context of large, high-dimensional problems with unknown dynamics has been particularly challenging. In this paper, we present a new Variational Lower Bound for IRL (VLB-IRL), which is derived under the framework of a probabilistic graphical model with an optimality node. Our method simultaneously learns the reward function and policy under the learned reward function by maximizing the lower bound, which is equivalent to minimizing the reverse Kullback-Leibler divergence between an approximated distribution of optimality given the reward function and the true distribution of optimality given trajectories. This leads to a new IRL method that learns a valid reward function such that the policy under the learned reward achieves expert-level performance on several known domains. Importantly, the method outperforms the existing state-of-the-art IRL algorithms on these domains by demonstrating better reward from the learned policy