Search Results for author: Xinyi Zhang

Found 29 papers, 7 papers with code

Gated Fusion Network for Joint Image Deblurring and Super-Resolution

2 code implementations27 Jul 2018 Xinyi Zhang, Hang Dong, Zhe Hu, Wei-Sheng Lai, Fei Wang, Ming-Hsuan Yang

Single-image super-resolution is a fundamental task for vision applications to enhance the image quality with respect to spatial resolution.

Computational Efficiency Deblurring +2

Accuracy vs. Efficiency: Achieving Both through FPGA-Implementation Aware Neural Architecture Search

no code implementations31 Jan 2019 Weiwen Jiang, Xinyi Zhang, Edwin H. -M. Sha, Lei Yang, Qingfeng Zhuge, Yiyu Shi, Jingtong Hu

In addition, with a performance abstraction model to analyze the latency of neural architectures without training, our framework can quickly prune architectures that do not satisfy the specification, leading to higher efficiency.

Neural Architecture Search

Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning

2 code implementations23 Apr 2019 Linsen Dong, Guanyu Gao, Xinyi Zhang, Liang-Yu Chen, Yonggang Wen

Model-Based Reinforcement Learning (MBRL) is one category of Reinforcement Learning (RL) algorithms which can improve sampling efficiency by modeling and approximating system dynamics.

Autonomous Driving Model-based Reinforcement Learning +2

Gated Fusion Network for Degraded Image Super Resolution

1 code implementation2 Mar 2020 Xinyi Zhang, Hang Dong, Zhe Hu, Wei-Sheng Lai, Fei Wang, Ming-Hsuan Yang

To address this problem, we propose a dual-branch convolutional neural network to extract base features and recovered features separately.

Image Super-Resolution

Multi-Scale Boosted Dehazing Network with Dense Feature Fusion

1 code implementation CVPR 2020 Hang Dong, Jinshan Pan, Lei Xiang, Zhe Hu, Xinyi Zhang, Fei Wang, Ming-Hsuan Yang

To address the issue of preserving spatial information in the U-Net architecture, we design a dense feature fusion module using the back-projection feedback scheme.

Image Dehazing

HEU Emotion: A Large-scale Database for Multi-modal Emotion Recognition in the Wild

no code implementations24 Jul 2020 Jing Chen, Chenhui Wang, Kejun Wang, Chaoqun Yin, Cong Zhao, Tao Xu, Xinyi Zhang, Ziqiang Huang, Meichen Liu, Tao Yang

Existing multimodal emotion databases in the real-world conditions are few and small, with a limited number of subjects and expressed in a single language.

Emotion Recognition Facial Expression Recognition +1

LA-HCN: Label-based Attention for Hierarchical Multi-label TextClassification Neural Network

no code implementations23 Sep 2020 Xinyi Zhang, Jiahao Xu, Charlie Soh, Lihui Chen

In this paper, we propose a Label-based Attention for Hierarchical Mutlti-label Text Classification Neural Network (LA-HCN), where the novel label-based attention module is designed to hierarchically extract important information from the text based on the labels from different hierarchy levels.

Multi Label Text Classification Multi-Label Text Classification +1

mSHINE: A Multiple-meta-paths Simultaneous Learning Framework for Heterogeneous Information Network Embedding

1 code implementation6 Apr 2021 Xinyi Zhang, Lihui Chen

To address this issue, a novel meta-path-based HIN representation learning framework named mSHINE is designed to simultaneously learn multiple node representations for different meta-paths.

Link Prediction Network Embedding +1

Thief, Beware of What Get You There: Towards Understanding Model Extraction Attack

no code implementations13 Apr 2021 Xinyi Zhang, Chengfang Fang, Jie Shi

We find the effectiveness of existing techniques significantly affected by the absence of pre-trained models.

Model extraction

Learning To Restore Hazy Video: A New Real-World Dataset and a New Method

no code implementations CVPR 2021 Xinyi Zhang, Hang Dong, Jinshan Pan, Chao Zhu, Ying Tai, Chengjie Wang, Jilin Li, Feiyue Huang, Fei Wang

On the other hand, the video dehazing algorithms, which can acquire more satisfying dehazing results by exploiting the temporal redundancy from neighborhood hazy frames, receive less attention due to the absence of the video dehazing datasets.

Image Dehazing

Speech Technology for Everyone: Automatic Speech Recognition for Non-Native English with Transfer Learning

no code implementations1 Oct 2021 Toshiko Shibano, Xinyi Zhang, Mia Taige Li, Haejin Cho, Peter Sullivan, Muhammad Abdul-Mageed

To address the performance gap of English ASR models on L2 English speakers, we evaluate fine-tuning of pretrained wav2vec 2. 0 models (Baevski et al., 2020; Xu et al., 2021) on L2-ARCTIC, a non-native English speech corpus (Zhao et al., 2018) under different training settings.

Automatic Speech Recognition Automatic Speech Recognition (ASR) +3

Blind Face Restoration via Integrating Face Shape and Generative Priors

no code implementations CVPR 2022 Feida Zhu, Junwei Zhu, Wenqing Chu, Xinyi Zhang, Xiaozhong Ji, Chengjie Wang, Ying Tai

Moreover, we introduce hybrid-level losses to jointly train the shape and generative priors together with other network parts such that these two priors better adapt to our blind face restoration task.

3D Reconstruction Blind Face Restoration

EF-Train: Enable Efficient On-device CNN Training on FPGA Through Data Reshaping for Online Adaptation or Personalization

no code implementations18 Feb 2022 Yue Tang, Xinyi Zhang, Peipei Zhou, Jingtong Hu

In this work, we design EF-Train, an efficient DNN training accelerator with a unified channel-level parallelism-based convolution kernel that can achieve end-to-end training on resource-limited low-power edge-level FPGAs.

Domain Adaptation

H2H: Heterogeneous Model to Heterogeneous System Mapping with Computation and Communication Awareness

1 code implementation29 Apr 2022 Xinyi Zhang, Cong Hao, Peipei Zhou, Alex Jones, Jingtong Hu

The heterogeneity in ML models comes from multi-sensor perceiving and multi-task learning, i. e., multi-modality multi-task (MMMT), resulting in diverse deep neural network (DNN) layers and computation patterns.

Multi-Task Learning

Eight Years of Face Recognition Research: Reproducibility, Achievements and Open Issues

no code implementations8 Aug 2022 Tiago de Freitas Pereira, Dominic Schmidli, Yu Linghu, Xinyi Zhang, Sébastien Marcel, Manuel Günther

With the popularity of deep learning and its capability to solve a huge variety of different problems, face recognition researchers have concentrated effort on creating better models under this paradigm.

Face Recognition Open Set Learning

SIAD: Self-supervised Image Anomaly Detection System

no code implementations8 Aug 2022 Jiawei Li, Chenxi Lan, Xinyi Zhang, Bolin Jiang, Yuqiu Xie, Naiqi Li, Yan Liu, Yaowei Li, Enze Huo, Bin Chen

To make a step forward, this paper outlines an automatic annotation system called SsaA, working in a self-supervised learning manner, for continuously making the online visual inspection in the manufacturing automation scenarios.

Anomaly Detection Cloud Computing +1

HySAGE: A Hybrid Static and Adaptive Graph Embedding Network for Context-Drifting Recommendations

no code implementations20 Aug 2022 Sichun Luo, Xinyi Zhang, Yuanzhang Xiao, Linqi Song

For example, in a mobile game recommendation, contextual features like locations, battery, and storage levels of mobile devices are frequently drifting over time.

Collaborative Filtering Graph Embedding

Unsupervised Surface Anomaly Detection with Diffusion Probabilistic Model

no code implementations ICCV 2023 Xinyi Zhang, Naiqi Li, Jiawei Li, Tao Dai, Yong Jiang, Shu-Tao Xia

Unsupervised surface anomaly detection aims at discovering and localizing anomalous patterns using only anomaly-free training samples.

Unsupervised Anomaly Detection

Transfer Learning for Bayesian Optimization: A Survey

no code implementations12 Feb 2023 Tianyi Bai, Yang Li, Yu Shen, Xinyi Zhang, Wentao Zhang, Bin Cui

A wide spectrum of design and decision problems, including parameter tuning, A/B testing and drug design, intrinsically are instances of black-box optimization.

Bayesian Optimization Transfer Learning

A Unified and Efficient Coordinating Framework for Autonomous DBMS Tuning

no code implementations10 Mar 2023 Xinyi Zhang, Zhuo Chang, Hong Wu, Yang Li, Jia Chen, Jian Tan, Feifei Li, Bin Cui

To tune different components for DBMS, a coordinating mechanism is needed to make the multiple agents cognizant of each other.

Thompson Sampling

PerFedRec++: Enhancing Personalized Federated Recommendation with Self-Supervised Pre-Training

no code implementations11 May 2023 Sichun Luo, Yuanzhang Xiao, Xinyi Zhang, Yang Liu, Wenbo Ding, Linqi Song

Each user learns a personalized model by combining the global federated model, the cluster-level federated model, and its own fine-tuned local model.

Federated Learning Graph Learning +3

Towards General and Efficient Online Tuning for Spark

no code implementations5 Sep 2023 Yang Li, Huaijun Jiang, Yu Shen, Yide Fang, Xiaofeng Yang, Danqing Huang, Xinyi Zhang, Wentao Zhang, Ce Zhang, Peng Chen, Bin Cui

The distributed data analytic system -- Spark is a common choice for processing massive volumes of heterogeneous data, while it is challenging to tune its parameters to achieve high performance.

Bayesian Optimization Meta-Learning

Elucidating the solution space of extended reverse-time SDE for diffusion models

1 code implementation12 Sep 2023 Qinpeng Cui, Xinyi Zhang, Zongqing Lu, Qingmin Liao

In this work, we formulate the sampling process as an extended reverse-time SDE (ER SDE), unifying prior explorations into ODEs and SDEs.

Image Generation

Learning Uniform Clusters on Hypersphere for Deep Graph-level Clustering

no code implementations23 Nov 2023 Mengling Hu, Chaochao Chen, Weiming Liu, Xinyi Zhang, Xinting Liao, Xiaolin Zheng

However, most existing graph clustering methods focus on node-level clustering, i. e., grouping nodes in a single graph into clusters.

Clustering Contrastive Learning +2

Integrating Large Language Models into Recommendation via Mutual Augmentation and Adaptive Aggregation

no code implementations25 Jan 2024 Sichun Luo, Yuxuan Yao, Bowei He, Yinya Huang, Aojun Zhou, Xinyi Zhang, Yuanzhang Xiao, Mingjie Zhan, Linqi Song

Conventional recommendation methods have achieved notable advancements by harnessing collaborative or sequential information from user behavior.

Data Augmentation

Biased Binary Attribute Classifiers Ignore the Majority Classes

no code implementations21 Mar 2024 Xinyi Zhang, Johanna Sophie Bieri, Manuel Günther

In this paper, we extend gradient-based CAM techniques to work with binary classifiers and visualize the active regions for binary facial attribute classifiers.

Attribute Binary Classification

基于自动识别的委婉语历时性发展变化与社会共变研究(A Study on the Diachronic Development and Social Covariance of Euphemism Based on Automatic Recognition)

no code implementations CCL 2021 Chenlin Zhang, Mingwen Wang, Yiming Tan, Ming Yin, Xinyi Zhang

“本文主要以汉语委婉语作为研究对象, 基于大量人工标注, 借助机器学习有监督分类方法, 实现了较高精度的委婉语自动识别, 并基于此对1946年-2017年的《人民日报》中的委婉语历时变化发展情况进行量化统计分析。从大规模数据的角度探讨委婉语历时性发展变化、委婉语与社会之间的共变关系, 验证了语言的格雷什姆规律与更新规律。”

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