activity
20132023
most citedLarge AI Models in Health Informatics: Applications, Challenges, and the Future

238 citations · 423 across the 30 of their papers we have counts for

collaborators

30 papers

cs.SD2023

ReFlow-TTS: A Rectified Flow Model for High-fidelity Text-to-Speech

Wenhao Guan, Qi Su, Haodong Zhou +4

The diffusion models including Denoising Diffusion Probabilistic Models (DDPM) and score-based generative models have demonstrated excellent performance in speech synthesis tasks.…

cs.SD2023★ 1 cited

Interpretable Style Transfer for Text-to-Speech with ControlVAE and Diffusion Bridge

Wenhao Guan, Tao Li, Yishuang Li +3

With the demand for autonomous control and personalized speech generation, the style control and transfer in Text-to-Speech (TTS) is becoming more and more important. In this paper…

cs.CV2023

RealLiFe: Real-Time Light Field Reconstruction via Hierarchical Sparse Gradient Descent

Yijie Deng, Lei Han, Tianpeng Lin +3

With the rise of Extended Reality (XR) technology, there is a growing need for real-time light field reconstruction from sparse view inputs. Existing methods can be classified into…

eess.AS2023★ 6 cited

Community Detection Graph Convolutional Network for Overlap-Aware Speaker Diarization

Jie Wang, Zhicong Chen, Haodong Zhou +2

The clustering algorithm plays a crucial role in speaker diarization systems. However, traditional clustering algorithms suffer from the complex distribution of speaker embeddings…

cs.CV2023★ 1 cited

AROID: Improving Adversarial Robustness Through Online Instance-Wise Data Augmentation

Lin Li, Jianing Qiu, Michael Spratling

Deep neural networks are vulnerable to adversarial examples. Adversarial training (AT) is an effective defense against adversarial examples. However, AT is prone to overfitting whi…

cs.CV2023★ 1 cited

Improved Adversarial Training Through Adaptive Instance-wise Loss Smoothing

Lin Li, Michael Spratling

Deep neural networks can be easily fooled into making incorrect predictions through corruption of the input by adversarial perturbations: human-imperceptible artificial noise. So f…