405 citations · 2.3k across the 116 of their papers we have counts for
13 papers · 2 filters
Towards Disentangled Representations for Human Retargeting by Multi-view Learning
Chao Yang, Xiaofeng Liu, Qingming Tang +1
We study the problem of learning disentangled representations for data across multiple domains and its applications in human retargeting. Our goal is to map an input image to an id…
PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation
Can Qin, Haoxuan You, Lichen Wang +2
Domain Adaptation (DA) approaches achieved significant improvements in a wide range of machine learning and computer vision tasks (i.e., classification, detection, and segmentation…
PointHop: An Explainable Machine Learning Method for Point Cloud Classification
Min Zhang, Haoxuan You, Pranav Kadam +2
An explainable machine learning method for point cloud classification, called the PointHop method, is proposed in this work. The PointHop method consists of two stages: 1) local-to…
An Interpretable Compression and Classification System: Theory and Applications
Tzu-Wei Tseng, Kai-Jiun Yang, C. -C. Jay Kuo +2
This study proposes a low-complexity interpretable classification system. The proposed system contains three main modules including feature extraction, feature reduction, and class…
Deep Kinship Verification via Appearance-shape Joint Prediction and Adaptation-based Approach
Heming Zhang, Xiaolong Wang, C. -C. Jay Kuo
Kinship verification aims to identify the kin relation between two given face images. It is a very challenging problem due to the lack of training data and facial similarity variat…
Accelerating Proposal Generation Network for \\Fast Face Detection on Mobile Devices
Heming Zhang, Xiaolong Wang, Jingwen Zhu +1
Face detection is a widely studied problem over the past few decades. Recently, significant improvements have been achieved via the deep neural network, however, it is still challe…
Visualization, Discriminability and Applications of Interpretable Saak Features
Abinaya Manimaran, Thiyagarajan Ramanathan, Suya You +1
In this work, we study the power of Saak features as an effort towards interpretable deep learning. Being inspired by the operations of convolutional layers of convolutional neural…
Class-incremental Learning via Deep Model Consolidation
Junting Zhang, Jie Zhang, Shalini Ghosh +5
Deep neural networks (DNNs) often suffer from "catastrophic forgetting" during incremental learning (IL) --- an abrupt degradation of performance on the original set of classes whe…
Robustness Of Saak Transform Against Adversarial Attacks
Thiyagarajan Ramanathan, Abinaya Manimaran, Suya You +1
Image classification is vulnerable to adversarial attacks. This work investigates the robustness of Saak transform against adversarial attacks towards high performance image classi…
Semi-supervised learning via Feedforward-Designed Convolutional Neural Networks
Yueru Chen, Yijing Yang, Min Zhang +1
A semi-supervised learning framework using the feedforward-designed convolutional neural networks (FF-CNNs) is proposed for image classification in this work. One unique property o…
Generative Visual Dialogue System via Adaptive Reasoning and Weighted Likelihood Estimation
Heming Zhang, Shalini Ghosh, Larry Heck +4
The key challenge of generative Visual Dialogue (VD) systems is to respond to human queries with informative answers in natural and contiguous conversation flow. Traditional Maximu…
Efficient Image Splicing Localization via Contrastive Feature Extraction
Ronald Salloum, C. -C. Jay Kuo
In this work, we propose a new data visualization and clustering technique for discovering discriminative structures in high-dimensional data. This technique, referred to as cPCA++…
Ensembles of feedforward-designed convolutional neural networks
Yueru Chen, Yijing Yang, Wei Wang +1
An ensemble method that fuses the output decision vectors of multiple feedforward-designed convolutional neural networks (FF-CNNs) to solve the image classification problem is prop…