activity
20172021
most citedA Bayesian Data Augmentation Approach for Learning Deep Models

118 citations · 123 across the 3 of their papers we have counts for

collaborators

7 papers

stat.ME2021

Ensemble approximate control variate estimators: Applications to multi-fidelity importance sampling

Trung Pham, Alex A. Gorodetsky

The recent growth in multi-fidelity uncertainty quantification has given rise to a large set of variance reduction techniques that leverage information from model ensembles to prov…

cs.CV2018

Bayesian Semantic Instance Segmentation in Open Set World

Trung Pham, Vijay Kumar B G, Thanh-Toan Do +2

This paper addresses the semantic instance segmentation task in the open-set conditions, where input images can contain known and unknown object classes. The training process of ex…

cs.RO2018

Structure Aware SLAM using Quadrics and Planes

Mehdi Hosseinzadeh, Yasir Latif, Trung Pham +2

Simultaneous Localization And Mapping (SLAM) is a fundamental problem in mobile robotics. While point-based SLAM methods provide accurate camera localization, the generated maps la…

cs.CV2018

Deep-6DPose: Recovering 6D Object Pose from a Single RGB Image

Thanh-Toan Do, Ming Cai, Trung Pham +1

Detecting objects and their 6D poses from only RGB images is an important task for many robotic applications. While deep learning methods have made significant progress in visual o…

cs.CV2018

Binary Constrained Deep Hashing Network for Image Retrieval without Manual Annotation

Thanh-Toan Do, Tuan Hoang, Dang-Khoa Le Tan +4

Learning compact binary codes for image retrieval task using deep neural networks has attracted increasing attention recently. However, training deep hashing networks for the task…

cs.CV2017118 cited

A Bayesian Data Augmentation Approach for Learning Deep Models

Toan Tran, Trung Pham, Gustavo Carneiro +2

Data augmentation is an essential part of the training process applied to deep learning models. The motivation is that a robust training process for deep learning models depends on…