Probabilistic Neural-Symbolic Models With Inductive Posterior Constraints.

Neural-symbolic models provide a powerful tool to tackle complex visual reasoning tasks by combining symbolic program execution for reasoning and deep representation learning for visual recognition. A probabilistic formulation of such models with stochastic latent variables can obtain an interpretable and legible reasoning system with less supervision. However, it is still nontrivial to generate reasonable symbolic structures without the guidance of domain knowledge, since it generally involves an optimization problem with both continuous and discrete variables.

Triple Generative Adversarial Networks.

We propose a unified game-theoretical framework to perform classification and conditional image generation given limited supervision. It is formulated as a three-player minimax game consisting of a generator, a classifier and a discriminator, and therefore is referred to as Triple Generative Adversarial Network (Triple-GAN). The generator and the classifier characterize the conditional distributions between images and labels to perform conditional generation and classification, respectively.

Max-Margin Deep Generative Models for (Semi-)Supervised Learning.

Deep generative models (DGMs) can effectively capture the underlying distributions of complex data by learning multilayered representations and performing inference. However, it is relatively insufficient to boost the discriminative ability of DGMs. This paper presents max-margin deep generative models (mmDGMs) and a class-conditional variant (mmDCGMs), which explore the strongly discriminative principle of max-margin learning to improve the predictive performance of DGMs in both supervised and semi-supervised learning, while retaining the generative capability.

Towards Better Analysis of Deep Convolutional Neural Networks.

Deep convolutional neural networks (CNNs) have achieved breakthrough performance in many pattern recognition tasks such as image classification. However, the development of high-quality deep models typically relies on a substantial amount of trial-and-error, as there is still no clear understanding of when and why a deep model works. In this paper, we present a visual analytics approach for better understanding, diagnosing, and refining deep CNNs.