6 papers
RL-I2IT: Image-to-Image Translation with Deep Reinforcement Learning
Jing Hu, Ziwei Luo, Chengming Feng +7
Most existing Image-to-Image Translation (I2IT) methods generate images in a single run of a deep learning (DL) model. However, designing such a single-step model is always challen…
A Self-Learning Multimodal Approach for Fake News Detection
Hao Chen, Hui Guo, Baochen Hu +5
The rapid growth of social media has resulted in an explosion of online news content, leading to a significant increase in the spread of misleading or false information. While mach…
Meta-Learning with Heterogeneous Tasks
Zhaofeng Si, Shu Hu, Kaiyi Ji +1
Meta-learning is a general approach to equip machine learning models with the ability to handle few-shot scenarios when dealing with many tasks. Most existing meta-learning methods…
CrossDF: Improving Cross-Domain Deepfake Detection with Deep Information Decomposition
Shanmin Yang, Hui Guo, Shu Hu +5
Deepfake technology poses a significant threat to security and social trust. Although existing detection methods have shown high performance in identifying forgeries within dataset…
An Explainable Non-local Network for COVID-19 Diagnosis
Jingfu Yang, Peng Huang, Jing Hu +5
The CNN has achieved excellent results in the automatic classification of medical images. In this study, we propose a novel deep residual 3D attention non-local network (NL-RAN) to…
Robustly Optimized Deep Feature Decoupling Network for Fatty Liver Diseases Detection
Peng Huang, Shu Hu, Bo Peng +3
Current medical image classification efforts mainly aim for higher average performance, often neglecting the balance between different classes. This can lead to significant differe…