no code implementations • 27 Feb 2023 • Wei Wang, Xuejing Lei, Yueru Chen, Ming-Sui Lee, C. -C. Jay Kuo
A light-weight super-resolution (LSR) method from a single image targeting mobile applications is proposed in this work.
no code implementations • 29 Oct 2020 • Yueru Chen, Yiting shao, Jing Wang, Ge Li, C. -C. Jay Kuo
Inspired by the recently proposed successive subspace learning (SSL) principles, we develop a successive subspace graph transform (SSGT) to address point cloud attribute compression in this work.
no code implementations • 8 Feb 2020 • Yueru Chen, Mozhdeh Rouhsedaghat, Suya You, Raghuveer Rao, C. -C. Jay Kuo
In PixelHop++, one can control the learning model size of fine-granularity, offering a flexible tradeoff between the model size and the classification performance.
2 code implementations • 17 Sep 2019 • Yueru Chen, C. -C. Jay Kuo
A new machine learning methodology, called successive subspace learning (SSL), is introduced in this work.
no code implementations • 6 Feb 2019 • Yueru Chen, Yijing Yang, Min Zhang, C. -C. Jay Kuo
A semi-supervised learning framework using the feedforward-designed convolutional neural networks (FF-CNNs) is proposed for image classification in this work.
no code implementations • 8 Jan 2019 • Yueru Chen, Yijing Yang, Wei Wang, C. -C. Jay Kuo
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 proposed in this work.
no code implementations • 19 Dec 2018 • Ye Wang, Jongmoo Choi, Yueru Chen, Siyang Li, Qin Huang, Kaitai Zhang, Ming-Sui Lee, C. -C. Jay Kuo
Unsupervised video object segmentation is a crucial application in video analysis without knowing any prior information about the objects.
no code implementations • 19 Dec 2018 • Ye Wang, Yueru Chen, Jongmoo Choi, C. -C. Jay Kuo
One is a model-based drone augmentation technique that automatically generates visible drone images with a bounding box label on the drone's location.
no code implementations • 13 Dec 2018 • Ye Wang, Jongmoo Choi, Yueru Chen, Qin Huang, Siyang Li, Ming-Sui Lee, C. -C. Jay Kuo
Experimental results on DAVIS and FBMS show that the proposed method outperforms state-of-the-art unsupervised segmentation methods on various benchmark datasets.
no code implementations • ICLR 2018 • 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 addressing the gradient vanishing/explosion problem, in this work.
no code implementations • 11 Nov 2018 • Yao Zhu, Saksham Suri, Pranav Kulkarni, Yueru Chen, Jiali Duan, C. -C. Jay Kuo
An interpretable generative model for handwritten digits synthesis is proposed in this work.
2 code implementations • 5 Oct 2018 • C. -C. Jay Kuo, Min Zhang, Siyang Li, Jiali Duan, Yueru Chen
To construct convolutional layers, we develop a new signal transform, called the Saab (Subspace Approximation with Adjusted Bias) transform.
no code implementations • 6 Aug 2018 • Sibo Song, Yueru Chen, Ngai-Man Cheung, C. -C. Jay Kuo
Therefore, we propose a Saak transform based preprocessing method with three steps: 1) transforming an input image to a joint spatial-spectral representation via the forward Saak transform, 2) apply filtering to its high-frequency components, and, 3) reconstructing the image via the inverse Saak transform.
no code implementations • 4 Dec 2017 • Yueru Chen, Pranav Aggarwal, Jongmoo Choi, C. -C. Jay Kuo
A drone monitoring system that integrates deep-learning-based detection and tracking modules is proposed in this work.
no code implementations • 29 Oct 2017 • Yueru Chen, Zhuwei Xu, Shanshan Cai, Yujian Lang, C. -C. Jay Kuo
We conduct a comparative study on the performance of the LeNet-5 and the Saak-transform-based solutions in terms of scalability and robustness as well as the efficiency of lossless and lossy Saak transforms under a comparable accuracy level.
2 code implementations • 11 Oct 2017 • C. -C. Jay Kuo, Yueru Chen
The Saak transform consists of three steps: 1) building the optimal linear subspace approximation with orthonormal bases using the second-order statistics of input vectors, 2) augmenting each transform kernel with its negative, 3) applying the rectified linear unit (ReLU) to the transform output.
no code implementations • 1 Feb 2017 • Xiaqing Pan, Yueru Chen, C. -C. Jay Kuo
In the design of the VCNN, we propose a feed-forward K-means clustering algorithm to determine the filter number and size at each convolutional layer systematically.
no code implementations • 30 Jan 2017 • Xiaqing Pan, Yueru Chen, C. -C. Jay Kuo
The remaining gallery samples are ranked in the SR module using the local feature.