Chung Nguyen Tran

Universitat Autònoma de Barcelona

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Co-Authors

Dong-Chul Park

Myongji University

Yun‐Sik Lee

Pusan National University

Dong-Chul Park

Myongji University

Le-Anh Tran

Myongji University

Byung-Jae Min

Myongji University

David Castells-Rufas

Universitat Autònoma de Barcelona

Jordi Carrabina

Universitat Autònoma de Barcelona

Youngsoo Song

Hanyang University

Sancho Park

Hwan-Soo Choi

Korea Advanced Institute of Science and Technology

Cooperative Institutions

Myongji University

Vietnam National University Ho Chi Minh City

Ho Chi Minh City University of Technology and Education

Korea Advanced Institute of Science and Technology

Korea Electronics Technology Institute

Ho Chi Minh City University of Science

Ho Chi Minh City University of Technology

Software (Spain)

Chungnam National University

University of Washington

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Content-Based Classification of Images Using Centroid Neural Network with Divergence Measure

Lecture notes in computer science (2006)

Dong-Chul Park Chung Nguyen Tran Yun‐Sik Lee

Centroid

Divergence (linguistics)

Robustness

10.1007/11941439_77

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Citations (3)

Embedding Clustering via Autoencoder and Projection onto Convex Set

Le-Anh Tran Thanh-Dat Nguyen Truong-Dong Do Chung Nguyen Tran Dae-Hyun Kwon

Projection onto Convex Set (POCS) is a powerful signal processing tool for various convex optimization problems. For non-intersecting convex sets, the simultaneous POCS method can result in a minimum mean square error solution. This property of POCS has been applied to clustering analysis and the POCS-based clustering algorithm was proposed earlier. In the POCS-based clustering algorithm, each data point is treated as a convex set, and a parallel projection operation from every cluster prototype to its corresponding data members is carried out in order to minimize the objective function and to update the memberships and prototypes. The algorithm works competitively against conventional clustering methods in terms of execution speed and clustering error on general clustering tasks. In this paper, the performance of the POCS-based clustering algorithm on a more complex task, embedding clustering, is investigated in order to further demonstrate its potential in benefiting other high-level tasks. To this end, an off-the-shelf FaceNet model and an autoencoder network are adopted to synthesize two sets of feature embeddings from the Five Celebrity Faces and MNIST datasets, respectively, for experiments and analyses. The empirical evaluations show that the POCS-based clustering algorithm can yield favorable results when compared with other prevailing clustering schemes such as the K-Means and Fuzzy C-Means algorithms in embedding clustering problems.

MNIST database

10.1109/icsse58758.2023.10227240

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Citations (2)

Fuzzy C-Means Algorithm with Divergence-Based Kernel

Lecture notes in computer science (2006)

Youngsoo Song Dong-Chul Park Chung Nguyen Tran Hwan-Soo Choi Minsoo Suk

Kernel (algebra)

Divergence (linguistics)

Feature vector

10.1007/11881599_12

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Citations (11)

Short-Term Load Forecasting Using Multiscale BiLinear Recurrent Neural Network

Springer eBooks (2006)

Dong-Chul Park Chung Nguyen Tran Yun‐Sik Lee

Multilayer perceptron

Perceptron

10.1007/11801603_36

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Citations (0)

Gradient Based Fuzzy C-Means Algorithm with a Mercer Kernel

Lecture notes in computer science (2006)

Dong-Chul Park Chung Nguyen Tran Sancho Park

Kernel (algebra)

10.1007/11759966_152

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Citations (4)

Modeling and Classification of Audio Signals Using Gradient-Based Fuzzy C-Means Algorithm with a Mercer Kernel

Lecture notes in computer science (2006)

Dong-Chul Park Chung Nguyen Tran Byung-Jae Min Sancho Park

Kernel (algebra)

Feature vector

10.1007/978-3-540-36668-3_142

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Citations (6)

Toward Improving Robustness of Object Detectors Against Domain Shift

arXiv (Cornell University) (2023)

Le-Anh Tran Chung Nguyen Tran Dong-Chul Park Jordi Carrabina David Castells-Rufas

This paper proposes a data augmentation method for improving the robustness of driving object detectors against domain shift. Domain shift problem arises when there is a significant change between the distribution of the source data domain used in the training phase and that of the target data domain in the deployment phase. Domain shift is known as one of the most popular reasons resulting in the considerable drop in the performance of deep neural network models. In order to address this problem, one effective approach is to increase the diversity of training data. To this end, we propose a data synthesis module that can be utilized to train more robust and effective object detectors. By adopting YOLOv4 as a base object detector, we have witnessed a remarkable improvement in performance on both the source and target domain data. The code of this work is publicly available at https://github.com/tranleanh/haze-synthesis.

Robustness

10.48550/arxiv.2403.12049

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Citations (0)

Toward Improving Robustness of Object Detectors against Domain Shift

Le-Anh Tran Chung Nguyen Tran Dong-Chul Park Jordi Carrabina David Castells-Rufas

Robustness

10.1109/gecost60902.2024.10474777

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Citations (1)

Modeling and Classification of Audio Signals Using Gradient-Based Fuzzy C-Means Algorithm with a Mercer Kernel

Springer eBooks (2006)