Search Results for author:
Found 11 papers, 0 papers with code
Robust Contrastive Learning With Theory Guarantee
Contrastive learning (CL) is a self-supervised training paradigm that allows us to extract meaningful features without any label information.
Sharpness & Shift-Aware Self-Supervised Learning
Self-supervised learning aims to extract meaningful features from unlabeled data for further downstream tasks.
Multiple Perturbation Attack: Attack Pixelwise Under Different $\ell_p$-norms For Better Adversarial Performance
On the other hand, in order to achieve that, we need to devise even stronger adversarial attacks to challenge these defense models.
Improving Multi-task Learning via Seeking Task-based Flat Regions
Multi-Task Learning (MTL) is a widely-used and powerful learning paradigm for training deep neural networks that allows learning more than one objective by a single backbone.
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.
Deep Learning Approach for Singer Voice Classification of Vietnamese Popular Music
Singer voice classification is a meaningful task in the digital era.
From Universal Language Model to Downstream Task: Improving RoBERTa-Based Vietnamese Hate Speech Detection
In this paper, we propose a pipeline to adapt the general-purpose RoBERTa language model to a specific text classification task: Vietnamese Hate Speech Detection.
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.
Efficient Low-Latency Dynamic Licensing for Deep Neural Network Deployment on Edge Devices
Among the two popular computing topologies for deploying neural network models in production are cloud-computing and edge-computing.
Efficient Palm-Line Segmentation with U-Net Context Fusion Module
In this paper, we propose an algorithm to extract principle palm lines from an image of a person's hand.
Deep Neural Networks based Invisible Steganography for Audio-into-Image Algorithm
In this paper, the use of deep learning techniques to hide secret audio into the digital images is proposed.
