Learning to navigate image manifolds induced by generative adversarial networks for unsupervised video generation

01/23/2019
by   Isabela Albuquerque, et al.
0

In this work, we introduce a two-step framework for generative modeling of temporal data. Specifically, the generative adversarial networks (GANs) setting is employed to generate synthetic scenes of moving objects. To do so, we propose a two-step training scheme within which: a generator of static frames is trained first. Afterwards, a recurrent model is trained with the goal of providing a sequence of inputs to the previously trained frames generator, thus yielding scenes which look natural. The adversarial setting is employed in both training steps. However, with the aim of avoiding known training instabilities in GANs, a multiple discriminator approach is used to train both models. Results in the studied video dataset indicate that, by employing such an approach, the recurrent part is able to learn how to coherently navigate the image manifold induced by the frames generator, thus yielding more natural-looking scenes.

READ FULL TEXT

page 3

page 4

research
10/25/2018

Training Generative Adversarial Networks Via Turing Test

In this article, we introduce a new mode for training Generative Adversa...
research
11/22/2020

Generative Adversarial Stacked Autoencoders

Generative Adversarial Networks (GANs) have become predominant in image ...
research
02/28/2021

Training Generative Adversarial Networks in One Stage

Generative Adversarial Networks (GANs) have demonstrated unprecedented s...
research
11/13/2017

ACtuAL: Actor-Critic Under Adversarial Learning

Generative Adversarial Networks (GANs) are a powerful framework for deep...
research
01/07/2019

Better Guider Predicts Future Better: Difference Guided Generative Adversarial Networks

Predicting the future is a fantasy but practicality work. It is the key ...
research
06/24/2019

GANalyze: Toward Visual Definitions of Cognitive Image Properties

We introduce a framework that uses Generative Adversarial Networks (GANs...
research
09/29/2016

Contextual RNN-GANs for Abstract Reasoning Diagram Generation

Understanding, predicting, and generating object motions and transformat...

Please sign up or login with your details

Forgot password? Click here to reset