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IJCNN Special Sessions
Oral
Deep and Generative Adversarial Learning

OptiGAN: Generative Adversarial Networks for Goal Optimized Sequence Generation

Mahmoud Hossam

Date & Time

Mon, July 20, 2020

5:45 pm – 7:45 pm

Location

On-Demand

Abstract

One of the challenging problems in sequence generation tasks is the optimized generation of sequences with specific desired goals. Current sequential generative models mainly generate sequences to closely mimic the training data, without direct optimization of desired goals or properties specific to the task. We introduce OptiGAN, a generative model that incorporates both Generative Adversarial Networks (GAN) and Reinforcement Learning (RL) to optimize desired goal scores using policy gradients. We apply our model to text and real-valued sequence generation, where our model is able to achieve higher desired scores out-performing GAN and RL baselines, while not sacrificing output sample diversity.


Presenter

Mahmoud Hossam

Monash University
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Session Chair

Ariel Ruiz-Garcia

Coventry University