21 citations · 50 across the 18 of their papers we have counts for
18 papers
LSR: A Light-Weight Super-Resolution Method
Wei Wang, Xuejing Lei, Yueru Chen +2
A light-weight super-resolution (LSR) method from a single image targeting mobile applications is proposed in this work. LSR predicts the residual image between the interpolated lo…
Point Cloud Attribute Compression via Successive Subspace Graph Transform
Yueru Chen, Yiting Shao, Jing Wang +2
Inspired by the recently proposed successive subspace learning (SSL) principles, we develop a successive subspace graph transform (SSGT) to address point cloud attribute compressio…
PixelHop++: A Small Successive-Subspace-Learning-Based (SSL-based) Model for Image Classification
Yueru Chen, Mozhdeh Rouhsedaghat, Suya You +2
The successive subspace learning (SSL) principle was developed and used to design an interpretable learning model, known as the PixelHop method,for image classification in our prio…
PixelHop: A Successive Subspace Learning (SSL) Method for Object Classification
Yueru Chen, C. -C. Jay Kuo
A new machine learning methodology, called successive subspace learning (SSL), is introduced in this work. SSL contains four key ingredients: 1) successive near-to-far neighborhood…
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…
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…
Towards Visible and Thermal Drone Monitoring with Convolutional Neural Networks
Ye Wang, Yueru Chen, Jongmoo Choi +1
This paper reports a visible and thermal drone monitoring system that integrates deep-learning-based detection and tracking modules. The biggest challenge in adopting deep learning…
Unsupervised Video Object Segmentation with Distractor-Aware Online Adaptation
Ye Wang, Jongmoo Choi, Yueru Chen +5
Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects. It becomes tremendously challenging when…
Design Pseudo Ground Truth with Motion Cue for Unsupervised Video Object Segmentation
Ye Wang, Jongmoo Choi, Yueru Chen +4
One major technique debt in video object segmentation is to label the object masks for training instances. As a result, we propose to prepare inexpensive, yet high quality pseudo g…
Analysis on Gradient Propagation in Batch Normalized Residual Networks
Abhishek Panigrahi, Yueru Chen, C. -C. Jay Kuo
We conduct mathematical analysis on the effect of batch normalization (BN) on gradient backpropogation in residual network training, which is believed to play a critical role in ad…
An Interpretable Generative Model for Handwritten Digit Image Synthesis
Yao Zhu, Saksham Suri, Pranav Kulkarni +3
An interpretable generative model for handwritten digits synthesis is proposed in this work. Modern image generative models, such as Generative Adversarial Networks (GANs) and Vari…
Interpretable Convolutional Neural Networks via Feedforward Design
C. -C. Jay Kuo, Min Zhang, Siyang Li +2
The model parameters of convolutional neural networks (CNNs) are determined by backpropagation (BP). In this work, we propose an interpretable feedforward (FF) design without any B…
Defense Against Adversarial Attacks with Saak Transform
Sibo Song, Yueru Chen, Ngai-Man Cheung +1
Deep neural networks (DNNs) are known to be vulnerable to adversarial perturbations, which imposes a serious threat to DNN-based decision systems. In this paper, we propose to appl…
A Deep Learning Approach to Drone Monitoring
Yueru Chen, Pranav Aggarwal, Jongmoo Choi +1
A drone monitoring system that integrates deep-learning-based detection and tracking modules is proposed in this work. The biggest challenge in adopting deep learning methods for d…
A Saak Transform Approach to Efficient, Scalable and Robust Handwritten Digits Recognition
Yueru Chen, Zhuwei Xu, Shanshan Cai +2
An efficient, scalable and robust approach to the handwritten digits recognition problem based on the Saak transform is proposed in this work. First, multi-stage Saak transforms ar…
On Data-Driven Saak Transform
C. -C. Jay Kuo, Yueru Chen
Being motivated by the multilayer RECOS (REctified-COrrelations on a Sphere) transform, we develop a data-driven Saak (Subspace approximation with augmented kernels) transform in t…
Design, Analysis and Application of A Volumetric Convolutional Neural Network
Xiaqing Pan, Yueru Chen, C. -C. Jay Kuo
The design, analysis and application of a volumetric convolutional neural network (VCNN) are studied in this work. Although many CNNs have been proposed in the literature, their de…
3D Shape Retrieval via Irrelevance Filtering and Similarity Ranking (IF/SR)
Xiaqing Pan, Yueru Chen, C. -C. Jay Kuo
A novel solution for the content-based 3D shape retrieval problem using an unsupervised clustering approach, which does not need any label information of 3D shapes, is presented in…