ReINTEL Challenge 2020: A Comparative Study of Hybrid Deep Neural Network for Reliable Intelligence Identification on Vietnamese SNSs

The overwhelming abundance of data has created a misinformation crisis. Unverified sensationalism that is designed to grab the readers' short attention span, when crafted with malice, has caused irreparable damage to our society's structure. As a result, determining the reliability of an article has become a crucial task. After various ablation studies, we propose a multi-input model that can effectively leverage both tabular metadata and post content for the task. Applying state-of-the-art finetuning techniques for the pretrained component and training strategies for our complete model, we have achieved a 0.9462 ROC-score on the VLSP private test set.

Interpreting the Latent Space of Generative Adversarial Networks using Supervised Learning

With great progress in the development of Generative Adversarial Networks (GANs), in recent years, the quest for insights in understanding and manipulating the latent space of GAN has gained more and more attention due to its wide range of applications. While most of the researches on this task have focused on unsupervised learning method, which induces difficulties in training and limitation in results, our work approaches another direction, encoding human's prior knowledge to discover more about the hidden space of GAN. With this supervised manner, we produce promising results, demonstrated by accurate manipulation of generated images. Even though our model is more suitable for task-specific problems, we hope that its ease in implementation, preciseness, robustness, and the allowance of richer set of properties (compared to other approaches) for image manipulation can enhance the result of many current applications.