Search Results for author: Sen Liu

Found 19 papers, 5 papers with code

OSP: Boosting Distributed Model Training with 2-stage Synchronization

no code implementations29 Jun 2023 Zixuan Chen, Lei Shi, Xuandong Liu, Jiahui Li, Sen Liu, Yang Xu

However, these two types of methods can result in accuracy loss due to discarded gradients and have limited enhancement on the throughput of model synchronization, respectively.

DSE-TTS: Dual Speaker Embedding for Cross-Lingual Text-to-Speech

no code implementations25 Jun 2023 Sen Liu, Yiwei Guo, Chenpeng Du, Xie Chen, Kai Yu

Although high-fidelity speech can be obtained for intralingual speech synthesis, cross-lingual text-to-speech (CTTS) is still far from satisfactory as it is difficult to accurately retain the speaker timbres(i. e. speaker similarity) and eliminate the accents from their first language(i. e. nativeness).

Speech Synthesis

HST: Hierarchical Swin Transformer for Compressed Image Super-resolution

3 code implementations21 Aug 2022 Bingchen Li, Xin Li, Yiting Lu, Sen Liu, Ruoyu Feng, Zhibo Chen

Compressed Image Super-resolution has achieved great attention in recent years, where images are degraded with compression artifacts and low-resolution artifacts.

Compressed Image Super-resolution Image Super-Resolution

RTN: Reinforced Transformer Network for Coronary CT Angiography Vessel-level Image Quality Assessment

no code implementations13 Jul 2022 Yiting Lu, Jun Fu, Xin Li, Wei Zhou, Sen Liu, Xinxin Zhang, Congfu Jia, Ying Liu, Zhibo Chen

Therefore, we propose a Progressive Reinforcement learning based Instance Discarding module (termed as PRID) to progressively remove quality-irrelevant/negative instances for CCTA VIQA.

Image Quality Assessment Multiple Instance Learning

Comprehensive process-molten pool relations modeling using CNN for wire-feed laser additive manufacturing

no code implementations22 Mar 2021 Noopur Jamnikar, Sen Liu, Craig Brice, Xiaoli Zhang

For the purpose of in situ quality control, the process parameters should be controlled in real-time based on sensed information from the process, in particular the molten pool.

Machine learning based in situ quality estimation by molten pool condition-quality relations modeling using experimental data

no code implementations21 Mar 2021 Noopur Jamnikar, Sen Liu, Craig Brice, Xiaoli Zhang

To enable in situ quality monitoring of bead geometry and characterization properties, we need to continuously monitor the sensor's data for molten pool dimensions and temperature for the Wire-feed laser additive manufacturing (WLAM) system.

BIG-bench Machine Learning

A Physics-Informed Machine Learning Model for Porosity Analysis in Laser Powder Bed Fusion Additive Manufacturing

no code implementations13 Jan 2021 Rui Liu, Sen Liu, Xiaoli Zhang

To address the first problem, a physics-informed, data-driven model (PIM), which instead of directly using machine setting parameters to predict porosity levels of printed parts, it first interprets machine settings into physical effects, such as laser energy density and laser radiation pressure.

BIG-bench Machine Learning Physics-informed machine learning

FAN: Frequency Aggregation Network for Real Image Super-resolution

no code implementations30 Sep 2020 Yingxue Pang, Xin Li, Xin Jin, Yaojun Wu, Jianzhao Liu, Sen Liu, Zhibo Chen

Specifically, we extract different frequencies of the LR image and pass them to a channel attention-grouped residual dense network (CA-GRDB) individually to output corresponding feature maps.

Image Super-Resolution SSIM

LIRA: Lifelong Image Restoration from Unknown Blended Distortions

no code implementations ECCV 2020 Jianzhao Liu, Jianxin Lin, Xin Li, Wei Zhou, Sen Liu, Zhibo Chen

Most existing image restoration networks are designed in a disposable way and catastrophically forget previously learned distortions when trained on a new distortion removal task.

Image Restoration SSIM

Physics-informed machine learning for composition-process-property alloy design: shape memory alloy demonstration

no code implementations4 Mar 2020 Sen Liu, Branden B. Kappes, Behnam Amin-ahmadi, Othmane Benafan, Xiaoli Zhang, Aaron P. Stebner

Machine learning (ML) is shown to predict new alloys and their performances in a high dimensional, multiple-target-property design space that considers chemistry, multi-step processing routes, and characterization methodology variations.

BIG-bench Machine Learning Feature Engineering +1

Region Normalization for Image Inpainting

1 code implementation23 Nov 2019 Tao Yu, Zongyu Guo, Xin Jin, Shilin Wu, Zhibo Chen, Weiping Li, Zhizheng Zhang, Sen Liu

In this work, we show that the mean and variance shifts caused by full-spatial FN limit the image inpainting network training and we propose a spatial region-wise normalization named Region Normalization (RN) to overcome the limitation.

Image Inpainting

Progressive Image Inpainting with Full-Resolution Residual Network

2 code implementations24 Jul 2019 Zongyu Guo, Zhibo Chen, Tao Yu, Jiale Chen, Sen Liu

Recently, learning-based algorithms for image inpainting achieve remarkable progress dealing with squared or irregular holes.

Image Inpainting

Distribution Discrepancy Maximization for Image Privacy Preserving

no code implementations18 Nov 2018 Sen Liu, Jianxin Lin, Zhibo Chen

Accordingly, we introduce a collaborative training scheme: a discriminator $D$ is trained to discriminate the reconstructed image from the encrypted image, and an encryption model $G_e$ is required to generate these two kinds of images to maximize the recognition rate of $D$, leading to the same training objective for both $D$ and $G_e$.

Privacy Preserving

Large-Scale Mapping of Human Activity using Geo-Tagged Videos

no code implementations24 Jun 2017 Yi Zhu, Sen Liu, Shawn Newsam

This paper is the first work to perform spatio-temporal mapping of human activity using the visual content of geo-tagged videos.

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