activity
20192026
most citedMulti-agent statistical discriminative sub-trajectory mining and an application to NBA basketball

6 citations · 20 across the 21 of their papers we have counts for

collaborators

31 papers

cs.CV2026

SpiS-GAN: Spiral-Modulated Handwriting Synthesis with Star Operation

Nguyen Duy Hieu, Dang Hoai Nam, Pham Hoang Giap +2

Training robust handwriting recognition (HTR) systems requires massive amounts of annotated data, which is often difficult to acquire. While synthetic handwriting generation offers…

cs.CV2025

HTR-ConvText: Leveraging Convolution and Textual Information for Handwritten Text Recognition

Pham Thach Thanh Truc, Dang Hoai Nam, Huynh Tong Dang Khoa +1

Handwritten Text Recognition remains challenging due to the limited data, high writing style variance, and scripts with complex diacritics. Existing approaches, though partially ad…

cs.CV2025

FW-GAN: Frequency-Driven Handwriting Synthesis with Wave-Modulated MLP Generator

Huynh Tong Dang Khoa, Dang Hoai Nam, Vo Nguyen Le Duy

Labeled handwriting data is often scarce, limiting the effectiveness of recognition systems that require diverse, style-consistent training samples. Handwriting synthesis offers a…

stat.ML2025

Statistical Inference for Autoencoder-based Anomaly Detection after Representation Learning-based Domain Adaptation

Tran Tuan Kiet, Nguyen Thang Loi, Vo Nguyen Le Duy

Anomaly detection (AD) plays a vital role across a wide range of domains, but its performance might deteriorate when applied to target domains with limited data. Domain Adaptation…

cs.CV2025

WriteViT: Handwritten Text Generation with Vision Transformer

Dang Hoai Nam, Huynh Tong Dang Khoa, Vo Nguyen Le Duy

Humans can quickly generalize handwriting styles from a single example by intuitively separating content from style. Machines, however, struggle with this task, especially in low-d…

stat.ML2025

Statistical Inference for Clustering-based Anomaly Detection

Nguyen Thi Minh Phu, Duong Tan Loc, Vo Nguyen Le Duy

Unsupervised anomaly detection (AD) is a fundamental problem in machine learning and statistics. A popular approach to unsupervised AD is clustering-based detection. However, this…