Search Results for author: C. L. Philip Chen

Found 34 papers, 7 papers with code

PivotMesh: Generic 3D Mesh Generation via Pivot Vertices Guidance

no code implementations27 May 2024 Haohan Weng, Yikai Wang, Tong Zhang, C. L. Philip Chen, Jun Zhu

Generating compact and sharply detailed 3D meshes poses a significant challenge for current 3D generative models.

FDCE-Net: Underwater Image Enhancement with Embedding Frequency and Dual Color Encoder

no code implementations27 Apr 2024 Zheng Cheng, Guodong Fan, Jingchun Zhou, Min Gan, C. L. Philip Chen

The FDCE-Net consists of two main structures: (1) Frequency Spatial Network (FS-Net) aims to achieve initial enhancement by utilizing our designed Frequency Spatial Residual Block (FSRB) to decouple image degradation factors in the frequency domain and enhance different attributes separately.

UIE

Spatial-frequency Dual-Domain Feature Fusion Network for Low-Light Remote Sensing Image Enhancement

no code implementations26 Apr 2024 Zishu Yao, Guodong Fan, Jinfu Fan, Min Gan, C. L. Philip Chen

Therefore, we propose a Dual-Domain Feature Fusion Network (DFFN) for low-light remote sensing image enhancement.

Image Enhancement

Desigen: A Pipeline for Controllable Design Template Generation

no code implementations CVPR 2024 Haohan Weng, Danqing Huang, Yu Qiao, Zheng Hu, Chin-Yew Lin, Tong Zhang, C. L. Philip Chen

In this paper, we present Desigen, an automatic template creation pipeline which generates background images as well as harmonious layout elements over the background.

SpirDet: Towards Efficient, Accurate and Lightweight Infrared Small Target Detector

no code implementations8 Feb 2024 Qianchen Mao, Qiang Li, Bingshu Wang, Yongjun Zhang, Tao Dai, C. L. Philip Chen

To tackle this challenge, we propose SpirDet, a novel approach for efficient detection of infrared small targets.

Decoder

AI-Based Energy Transportation Safety: Pipeline Radial Threat Estimation Using Intelligent Sensing System

no code implementations18 Dec 2023 Chengyuan Zhu, Yiyuan Yang, Kaixiang Yang, Haifeng Zhang, Qinmin Yang, C. L. Philip Chen

This refinement is crucial in effectively identifying genuine threats to pipelines, thus enhancing the safety of energy transportation.

Transfer Learning

BroadCAM: Outcome-agnostic Class Activation Mapping for Small-scale Weakly Supervised Applications

1 code implementation7 Sep 2023 Jiatai Lin, Guoqiang Han, Xuemiao Xu, Changhong Liang, Tien-Tsin Wong, C. L. Philip Chen, Zaiyi Liu, Chu Han

Class activation mapping~(CAM), a visualization technique for interpreting deep learning models, is now commonly used for weakly supervised semantic segmentation~(WSSS) and object localization~(WSOL).

Object Localization Weakly supervised Semantic Segmentation +1

Rethinking Client Drift in Federated Learning: A Logit Perspective

no code implementations20 Aug 2023 Yunlu Yan, Chun-Mei Feng, Mang Ye, WangMeng Zuo, Ping Li, Rick Siow Mong Goh, Lei Zhu, C. L. Philip Chen

Concretely, FedCSD introduces a class prototype similarity distillation to align the local logits with the refined global logits that are weighted by the similarity between local logits and the global prototype.

Federated Learning

High-Similarity-Pass Attention for Single Image Super-Resolution

no code implementations25 May 2023 Jian-Nan Su, Min Gan, Guang-Yong Chen, Wenzhong Guo, C. L. Philip Chen

Based on these findings, we introduced a concise yet effective soft thresholding operation to obtain high-similarity-pass attention (HSPA), which is beneficial for generating a more compact and interpretable distribution.

Image Super-Resolution

Properties and Potential Applications of Random Functional-Linked Types of Neural Networks

no code implementations3 Apr 2023 Guang-Yong Chen, Yong-Hang Yu, Min Gan, C. L. Philip Chen, Wenzhong Guo

Random functional-linked types of neural networks (RFLNNs), e. g., the extreme learning machine (ELM) and broad learning system (BLS), which avoid suffering from a time-consuming training process, offer an alternative way of learning in deep structure.

ConvBLS: An Effective and Efficient Incremental Convolutional Broad Learning System for Image Classification

no code implementations1 Apr 2023 Chunyu Lei, C. L. Philip Chen, Jifeng Guo, Tong Zhang

Third, the TSMS feature fusion layer is proposed to extract more effective multi-scale features through the integration of CF layers and CE layers.

Image Classification Incremental Learning

FedDBL: Communication and Data Efficient Federated Deep-Broad Learning for Histopathological Tissue Classification

1 code implementation24 Feb 2023 Tianpeng Deng, Yanqi Huang, Guoqiang Han, Zhenwei Shi, Jiatai Lin, Qi Dou, Zaiyi Liu, Xiao-jing Guo, C. L. Philip Chen, Chu Han

In this paper, we propose a universal and lightweight federated learning framework, named Federated Deep-Broad Learning (FedDBL), to achieve superior classification performance with limited training samples and only one-round communication.

Federated Learning

Global Learnable Attention for Single Image Super-Resolution

1 code implementation2 Dec 2022 Jian-Nan Su, Min Gan, Guang-Yong Chen, Jia-Li Yin, C. L. Philip Chen

Utilizing this finding, we proposed a Global Learnable Attention (GLA) to adaptively modify similarity scores of non-local textures during training instead of only using a fixed similarity scoring function such as the dot product.

Image Super-Resolution

Broad Recommender System: An Efficient Nonlinear Collaborative Filtering Approach

1 code implementation20 Apr 2022 Ling Huang, Can-Rong Guan, Zhen-Wei Huang, Yuefang Gao, Yingjie Kuang, Chang-Dong Wang, C. L. Philip Chen

Recently, Deep Neural Networks (DNNs) have been widely introduced into Collaborative Filtering (CF) to produce more accurate recommendation results due to their capability of capturing the complex nonlinear relationships between items and users. However, the DNNs-based models usually suffer from high computational complexity, i. e., consuming very long training time and storing huge amount of trainable parameters.

Collaborative Filtering Recommendation Systems

A Novel Multi-Task Learning Method for Symbolic Music Emotion Recognition

no code implementations15 Jan 2022 Jibao Qiu, C. L. Philip Chen, Tong Zhang

In this paper, we present a simple multi-task framework for SMER, which incorporates the emotion recognition task with other emotion-related auxiliary tasks derived from the intrinsic structure of the music.

Emotion Recognition Language Modelling +2

OneDConv: Generalized Convolution For Transform-Invariant Representation

no code implementations15 Jan 2022 Tong Zhang, Haohan Weng, Ke Yi, C. L. Philip Chen

Convolutional Neural Networks (CNNs) have exhibited their great power in a variety of vision tasks.

A Survey on Masked Facial Detection Methods and Datasets for Fighting Against COVID-19

no code implementations13 Jan 2022 Bingshu Wang, Jiangbin Zheng, C. L. Philip Chen

Representative algorithms are described in detail, coupled with some typical techniques that are described briefly.

Benchmarking Lesion Segmentation

Graph Representation Learning via Contrasting Cluster Assignments

no code implementations15 Dec 2021 ChunYang Zhang, Hongyu Yao, C. L. Philip Chen, Yuena Lin

With the rise of contrastive learning, unsupervised graph representation learning has been booming recently, even surpassing the supervised counterparts in some machine learning tasks.

Clustering Contrastive Learning +1

Stacked BNAS: Rethinking Broad Convolutional Neural Network for Neural Architecture Search

no code implementations15 Nov 2021 Zixiang Ding, Yaran Chen, Nannan Li, Dongbin Zhao, C. L. Philip Chen

Moreover, multi-scale feature fusion and knowledge embedding are proposed to improve the performance of BCNN with shallow topology.

Neural Architecture Search

Siamese Labels Auxiliary Learning

no code implementations27 Feb 2021 Wenrui Gan, Zhulin Liu, C. L. Philip Chen, Tong Zhang

In general, the main work of this paper include: (1) propose SiLa Learning, which improves the performance of common models without increasing test parameters; (2) compares SiLa with DML and proves that SiLa can improve the generalization of the model; (3) SiLa is applied to Dynamic Neural Networks, and proved that SiLa can be used for various types of network structures.

Auxiliary Learning

Towards Uncovering the Intrinsic Data Structures for Unsupervised Domain Adaptation using Structurally Regularized Deep Clustering

2 code implementations8 Dec 2020 Hui Tang, Xiatian Zhu, Ke Chen, Kui Jia, C. L. Philip Chen

To address this issue, we are motivated by a UDA assumption of structural similarity across domains, and propose to directly uncover the intrinsic target discrimination via constrained clustering, where we constrain the clustering solutions using structural source regularization that hinges on the very same assumption.

Constrained Clustering Deep Clustering +3

Modal Regression based Structured Low-rank Matrix Recovery for Multi-view Learning

no code implementations22 Mar 2020 Jiamiao Xu, Fangzhao Wang, Qinmu Peng, Xinge You, Shuo Wang, Xiao-Yuan Jing, C. L. Philip Chen

Furthermore, recent low-rank modeling provides a satisfactory solution to address data contaminated by predefined assumptions of noise distribution, such as Gaussian or Laplacian distribution.

MULTI-VIEW LEARNING regression +1

BNAS:An Efficient Neural Architecture Search Approach Using Broad Scalable Architecture

no code implementations18 Jan 2020 Zixiang Ding, Yaran Chen, Nannan Li, Dongbin Zhao, Zhiquan Sun, C. L. Philip Chen

In this paper, we propose Broad Neural Architecture Search (BNAS) where we elaborately design broad scalable architecture dubbed Broad Convolutional Neural Network (BCNN) to solve the above issue.

Neural Architecture Search reinforcement-learning +1

Geometry-Aware Generation of Adversarial Point Clouds

2 code implementations24 Dec 2019 Yuxin Wen, Jiehong Lin, Ke Chen, C. L. Philip Chen, Kui Jia

Regularizing the targeted attack loss with our proposed geometry-aware objectives results in our proposed method, Geometry-Aware Adversarial Attack ($GeoA^3$).

Adversarial Attack Fairness

Reducing the Computational Complexity of Pseudoinverse for the Incremental Broad Learning System on Added Inputs

no code implementations17 Oct 2019 Hufei Zhu, Zhulin Liu, C. L. Philip Chen, Yanyang Liang

Specifically, when q > k, the proposed algorithm computes only a k * k matrix inverse, instead of a q * q matrix inverse in the existing algorithm.

Incremental Learning

Multi Pseudo Q-learning Based Deterministic Policy Gradient for Tracking Control of Autonomous Underwater Vehicles

no code implementations7 Sep 2019 Wenjie Shi, Shiji Song, Cheng Wu, C. L. Philip Chen

Different from existing policy gradient methods which employ single actor-critic but cannot realize satisfactory tracking control accuracy and stable learning, our proposed algorithm can achieve high-level tracking control accuracy of AUVs and stable learning by applying a hybrid actors-critics architecture, where multiple actors and critics are trained to learn a deterministic policy and action-value function, respectively.

Policy Gradient Methods Q-Learning

An Effective Background Estimation Method for Shadows Removal of Document Images

1 code implementation ICIP 2019 Bingshu Wang, C. L. Philip Chen

This paper proposes an effective method to remove shadows from the single document images, which contains two stages: shadow detection and shadow removal.

Document Shadow Removal Shadow Detection

3DViewGraph: Learning Global Features for 3D Shapes from A Graph of Unordered Views with Attention

no code implementations17 May 2019 Zhizhong Han, Xiyang Wang, Chi-Man Vong, Yu-Shen Liu, Matthias Zwicker, C. L. Philip Chen

Then, the content and spatial information of each pair of view nodes are encoded by a novel spatial pattern correlation, where the correlation is computed among latent semantic patterns.

Multi-view Hybrid Embedding: A Divide-and-Conquer Approach

no code implementations19 Apr 2018 Jiamiao Xu, Shujian Yu, Xinge You, Mengjun Leng, Xiao-Yuan Jing, C. L. Philip Chen

We present a novel cross-view classification algorithm where the gallery and probe data come from different views.

Classification General Classification

A Many-Objective Evolutionary Algorithm With Two Interacting Processes: Cascade Clustering and Reference Point Incremental Learning

no code implementations3 Mar 2018 Hongwei Ge, Mingde Zhao, Liang Sun, Zhen Wang, Guozhen Tan, Qiang Zhang, C. L. Philip Chen

This paper proposes a many-objective optimization algorithm with two interacting processes: cascade clustering and reference point incremental learning (CLIA).

Clustering Diversity +1

Broad Learning System: An Effective and Efficient Incremental Learning System Without the Need for Deep Architecture

no code implementations IEEE Transactions on Neural Networks and Learning Systems 2017 C. L. Philip Chen, Zhulin Liu

The BLS is established in the form of a flat network, where the original inputs are transferred and placed as “mapped features” in feature nodes and the structure is expanded in wide sense in the “enhancement nodes.” The incremental learning algorithms are developed for fast remodeling in broad expansion without a retraining process if the network deems to be expanded.

Incremental Learning Object Recognition

Approximation learning methods of Harmonic Mappings in relation to Hardy Spaces

no code implementations24 May 2017 Zhulin Liu, C. L. Philip Chen

A new Hardy space Hardy space approach of Dirichlet type problem based on Tikhonov regularization and Reproducing Hilbert kernel space is discussed in this paper, which turns out to be a typical extremal problem located on the upper upper-high complex plane.

Relation

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