Generative Adversarial Networks (GANs) are one of the most popular and powerful models to learn complex high dimensional distributions. However, they usually suffer from instability generalization issues which may lead poor generations. Most existing works focus on stabilizing training for discriminators GANs while ignoring their issue. In this work, we aim improve capability by promoting local...

Network neuroscience is the emerging discipline concerned with investigating the complex patterns of interconnections found in neural systems, and identifying principles with which to understand them. Within this discipline, one particularly powerful approach is network generative modelling, in which wiring rules are algorithmically implemented to produce synthetic network architectures with th...

Generating identity-preserving faces aims to generate various face images keeping the same identity given a target face image. Although considerable generative models have been developed in recent years, it is still challenging to simultaneously acquire high quality of facial images and preserve the identity. Here we propose a compelling method using generative adversarial networks (GAN). Concr...

Generative adversarial networks (GANs) are a framework for producing a generative model by way of a two-player minimax game. In this paper, we propose the Generative Multi-Adversarial Network (GMAN), a framework that extends GANs to multiple discriminators. In previous work, the successful training of GANs requires modifying the minimax objective to accelerate training early on. In contrast, GM...

This paper presents an adaptive visual learning algorithm for object tracking. We formulate a novel discriminative generative framework that generalizes the conventional Fisher Linear Discriminant algorithm with a generative model and renders a proper probabilistic interpretation. Within the context of object tracking, we aim to find a discriminative generative model that best separates the tar...

Generative models provide a way to model structure in complex distributions and have been shown to be useful for many tasks of practical interest. However, current techniques for training generative models require access to fully-observed samples. In many settings, it is expensive or even impossible to obtain fullyobserved samples, but economical to obtain partial, noisy observations. We consid...

Attention has long been proposed by psychologists to be important for efficiently dealing with the massive amounts of sensory stimulus in the neocortex. Inspired by the attention models in visual neuroscience and the need for object-centered data for generative models, we propose a deep-learning based generative framework using attention. The attentional mechanism propagates signals from the re...

Generative modeling techniques are being rapidly developed in the field of deep learning, and they have been applied to topology optimization. The variational autoencoder (VAE) is a generative modeling technology that extends the autoencoder to generate new images with a limited latent space. We modified the basic VAE structure to encode optimization conditions and decode latent variables for t...

Introduction .....................................................................................................................................................................112 Making Meaning in Generative Learning.......................................................................................................................112 Generative Learning Foundations ..........................

In this paper we demonstrate the computational benefits of a radically lexicalist generative grammar. We have developed a Prolog-parser on the basis of the new approach of Totally Lexicalist Morphology (TLM), which is developed out of Generative Argument Structure Grammar (GASG; [2]), a new and radical version of lexicalist generative grammar (in the spirit of e.g. Karttunen [5]). The parser de...