19 citations · 73 across the 28 of their papers we have counts for
32 papers
Post-Anomaly Detection Inference for Deep SVDD
Cao Le Cong Thanh, Dang Quang Vinh, Vo Nguyen Le Duy
Deep Support Vector Data Description (Deep SVDD) has become a prominent framework for unsupervised anomaly detection by learning latent representations that compactly characterize…
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…
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…
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…
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…
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…
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…
Post-Transfer Learning Statistical Inference in High-Dimensional Regression
Nguyen Vu Khai Tam, Cao Huyen My, Vo Nguyen Le Duy
Transfer learning (TL) for high-dimensional regression (HDR) is an important problem in machine learning, particularly when dealing with limited sample size in the target task. How…
Multi-level Supervised Contrastive Learning
Naghmeh Ghanooni, Barbod Pajoum, Harshit Rawal +3
Contrastive learning is a well-established paradigm in representation learning. The standard framework of contrastive learning minimizes the distance between "similar" instances an…
Statistical Inference for Sequential Feature Selection after Domain Adaptation
Duong Tan Loc, Nguyen Thang Loi, Vo Nguyen Le Duy
In high-dimensional regression, feature selection methods, such as sequential feature selection (SeqFS), are commonly used to identify relevant features. When data is limited, doma…
Adaptive Two-Phase Finetuning LLMs for Japanese Legal Text Retrieval
Quang Hoang Trung, Nguyen Van Hoang Phuc, Le Trung Hoang +2
Text Retrieval (TR) involves finding and retrieving text-based content relevant to a user's query from a large repository, with applications in real-world scenarios such as legal d…
Controllable RANSAC-based Anomaly Detection via Hypothesis Testing
Le Hong Phong, Ho Ngoc Luat, Vo Nguyen Le Duy
Detecting the presence of anomalies in regression models is a crucial task in machine learning, as anomalies can significantly impact the accuracy and reliability of predictions. R…
Statistical Inference for Feature Selection after Optimal Transport-based Domain Adaptation
Nguyen Thang Loi, Duong Tan Loc, Vo Nguyen Le Duy
Feature Selection (FS) under domain adaptation (DA) is a critical task in machine learning, especially when dealing with limited target data. However, existing methods lack the cap…
Statistical Test for Diffusion-Based Anomaly Localization via Selective Inference
Teruyuki Katsuoka, Tomohiro Shiraishi, Daiki Miwa +2
Anomaly localization in images -- identifying regions that deviate from normal patterns -- is vital in applications such as medical diagnosis and industrial inspection. A recent tr…
Statistical Test for Anomaly Detections by Variational Auto-Encoders
Daiki Miwa, Tomohiro Shiraishi, Vo Nguyen Le Duy +2
In this study, we consider the reliability assessment of anomaly detection (AD) using Variational Autoencoder (VAE). Over the last decade, VAE-based AD has been actively studied in…
Statistical Test for Attention Map in Vision Transformer
Tomohiro Shiraishi, Daiki Miwa, Teruyuki Katsuoka +3
The Vision Transformer (ViT) demonstrates exceptional performance in various computer vision tasks. Attention is crucial for ViT to capture complex wide-ranging relationships among…
Multi-agent statistical discriminative sub-trajectory mining and an application to NBA basketball
Rory Bunker, Vo Nguyen Le Duy, Yasuo Tabei +2
Improvements in tracking technology through optical and computer vision systems have enabled a greater understanding of the movement-based behaviour of multiple agents, including i…
Selective Inference for Changepoint detection by Recurrent Neural Network
Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy +1
In this study, we investigate the quantification of the statistical reliability of detected change points (CPs) in time series using a Recurrent Neural Network (RNN). Thanks to its…
CAD-DA: Controllable Anomaly Detection after Domain Adaptation by Statistical Inference
Vo Nguyen Le Duy, Hsuan-Tien Lin, Ichiro Takeuchi
We propose a novel statistical method for testing the results of anomaly detection (AD) under domain adaptation (DA), which we call CAD-DA -- controllable AD under DA. The distinct…
Bounded P-values in Parametric Programming-based Selective Inference
Tomohiro Shiraishi, Daiki Miwa, Vo Nguyen Le Duy +1
Selective inference (SI) has been actively studied as a promising framework for statistical hypothesis testing for data-driven hypotheses. The basic idea of SI is to make inference…
Valid P-Value for Deep Learning-Driven Salient Region
Daiki Miwa, Vo Nguyen Le Duy, Ichiro Takeuchi
Various saliency map methods have been proposed to interpret and explain predictions of deep learning models. Saliency maps allow us to interpret which parts of the input signals h…
Statistical Inference for the Dynamic Time Warping Distance, with Application to Abnormal Time-Series Detection
Vo Nguyen Le Duy, Ichiro Takeuchi
We study statistical inference on the similarity/distance between two time-series under uncertain environment by considering a statistical hypothesis test on the distance obtained…
Valid and Exact Statistical Inference for Multi-dimensional Multiple Change-Points by Selective Inference
Ryota Sugiyama, Hiroki Toda, Vo Nguyen Le Duy +2
In this paper, we study statistical inference of change-points (CPs) in multi-dimensional sequence. In CP detection from a multi-dimensional sequence, it is often desirable not onl…
Exact Statistical Inference for the Wasserstein Distance by Selective Inference
Vo Nguyen Le Duy, Ichiro Takeuchi
In this paper, we study statistical inference for the Wasserstein distance, which has attracted much attention and has been applied to various machine learning tasks. Several studi…