Search Results for author: Shuo Han

Found 17 papers, 6 papers with code

Improving Perceptual Quality, Intelligibility, and Acoustics on VoIP Platforms

no code implementations16 Mar 2023 Joseph Konan, Ojas Bhargave, Shikhar Agnihotri, Hojeong Lee, Ankit Shah, Shuo Han, Yunyang Zeng, Amanda Shu, Haohui Liu, Xuankai Chang, Hamza Khalid, Minseon Gwak, Kawon Lee, Minjeong Kim, Bhiksha Raj

In this paper, we present a method for fine-tuning models trained on the Deep Noise Suppression (DNS) 2020 Challenge to improve their performance on Voice over Internet Protocol (VoIP) applications.

Multi-Task Learning Speech Enhancement +2

PAAPLoss: A Phonetic-Aligned Acoustic Parameter Loss for Speech Enhancement

2 code implementations16 Feb 2023 Muqiao Yang, Joseph Konan, David Bick, Yunyang Zeng, Shuo Han, Anurag Kumar, Shinji Watanabe, Bhiksha Raj

We can add this criterion as an auxiliary loss to any model that produces speech, to optimize speech outputs to match the values of clean speech in these features.

Speech Enhancement Time Series Analysis

Quantitative Planning with Action Deception in Concurrent Stochastic Games

no code implementations3 Jan 2023 Chongyang Shi, Shuo Han, Jie Fu

In this setup, we investigate P1's strategic planning of action deception that decides when to deviate from the Nash equilibrium in P2's game model and employ a hidden action, so that P1 can maximize the value of action deception, which is the additional payoff compared to P1's payoff in the game where P2 has complete information.

Motion Planning

What is the Solution for State-Adversarial Multi-Agent Reinforcement Learning?

1 code implementation6 Dec 2022 Songyang Han, Sanbao Su, Sihong He, Shuo Han, Haizhao Yang, Fei Miao

Additionally, we propose a Robust Multi-Agent Adversarial Actor-Critic (RMA3C) algorithm to learn robust policies for MARL agents under state uncertainties.

Multi-agent Reinforcement Learning reinforcement-learning +1

Masked Autoencoders for Low dose CT denoising

no code implementations10 Oct 2022 Dayang Wang, Yongshun Xu, Shuo Han, Hengyong Yu

A plethora of transformer models have been developed recently to improve LDCT image quality.


A Robust and Constrained Multi-Agent Reinforcement Learning Framework for Electric Vehicle AMoD Systems

no code implementations17 Sep 2022 Sihong He, Yue Wang, Shuo Han, Shaofeng Zou, Fei Miao

We then propose a robust and constrained MARL algorithm (ROCOMA) that trains a robust EV rebalancing policy to balance the supply-demand ratio and the charging utilization rate across the whole city under state transition uncertainty.

Fairness Multi-agent Reinforcement Learning +1

Deep filter bank regression for super-resolution of anisotropic MR brain images

no code implementations6 Sep 2022 Samuel W. Remedios, Shuo Han, Yuan Xue, Aaron Carass, Trac D. Tran, Dzung L. Pham, Jerry L. Prince

In 2D multi-slice magnetic resonance (MR) acquisition, the through-plane signals are typically of lower resolution than the in-plane signals.

regression Super-Resolution

Disentangling A Single MR Modality

no code implementations10 May 2022 Lianrui Zuo, Yihao Liu, Yuan Xue, Shuo Han, Murat Bilgel, Susan M. Resnick, Jerry L. Prince, Aaron Carass

Disentangling anatomical and contrast information from medical images has gained attention recently, demonstrating benefits for various image analysis tasks.

Anatomy Disentanglement +3

Coordinate Translator for Learning Deformable Medical Image Registration

1 code implementation5 Mar 2022 Yihao Liu, Lianrui Zuo, Shuo Han, Yuan Xue, Jerry L. Prince, Aaron Carass

The majority of deep learning (DL) based deformable image registration methods use convolutional neural networks (CNNs) to estimate displacement fields from pairs of moving and fixed images.

Deformable Medical Image Registration Image Registration +1

MR Slice Profile Estimation by Learning to Match Internal Patch Distributions

1 code implementation31 Mar 2021 Shuo Han, Samuel Remedios, Aaron Carass, Michael Schär, Jerry L. Prince

To super-resolve the through-plane direction of a multi-slice 2D magnetic resonance (MR) image, its slice selection profile can be used as the degeneration model from high resolution (HR) to low resolution (LR) to create paired data when training a supervised algorithm.


CHAOS Challenge -- Combined (CT-MR) Healthy Abdominal Organ Segmentation

1 code implementation17 Jan 2020 A. Emre Kavur, N. Sinem Gezer, Mustafa Barış, Sinem Aslan, Pierre-Henri Conze, Vladimir Groza, Duc Duy Pham, Soumick Chatterjee, Philipp Ernst, Savaş Özkan, Bora Baydar, Dmitry Lachinov, Shuo Han, Josef Pauli, Fabian Isensee, Matthias Perkonigg, Rachana Sathish, Ronnie Rajan, Debdoot Sheet, Gurbandurdy Dovletov, Oliver Speck, Andreas Nürnberger, Klaus H. Maier-Hein, Gözde BOZDAĞI AKAR, Gözde Ünal, Oğuz Dicle, M. Alper Selver

The analysis shows that the performance of DL models for single modality (CT / MR) can show reliable volumetric analysis performance (DICE: 0. 98 $\pm$ 0. 00 / 0. 95 $\pm$ 0. 01) but the best MSSD performance remain limited (21. 89 $\pm$ 13. 94 / 20. 85 $\pm$ 10. 63 mm).

Organ Segmentation

Three Dimensional Fluorescence Microscopy Image Synthesis and Segmentation

no code implementations22 Jan 2018 Chichen Fu, Soonam Lee, David Joon Ho, Shuo Han, Paul Salama, Kenneth W. Dunn, Edward J. Delp

Advances in fluorescence microscopy enable acquisition of 3D image volumes with better image quality and deeper penetration into tissue.

Image Generation

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