no code implementations • 24 Dec 2022 • Cuiying Huo, Di Jin, Yawen Li, Dongxiao He, Yu-Bin Yang, Lingfei Wu
A key premise for the remarkable performance of GNNs relies on complete and trustworthy initial graph descriptions (i. e., node features and graph structure), which is often not satisfied since real-world graphs are often incomplete due to various unavoidable factors.
no code implementations • 24 May 2022 • Xin-Yi Li, Wei-Jun Lei, Yu-Bin Yang
Specifically, we first select the document most relevant to the question and then utilize the question together with this document to select other pertinent documents.
2 code implementations • 15 Apr 2022 • Qing-Long Zhang, Yu-Bin Yang
This paper proposes ResTv2, a simpler, faster, and stronger multi-scale vision Transformer for visual recognition.
no code implementations • 13 Sep 2019 • Jiangjun Tang, George Leu, Yu-Bin Yang
Results are encouraging, showing a good evolution of the fitness function used as part of the differential evolution, and a good performance of the evolved dual-swarm system, which exhibits an optimal trade-off between target reaching and connectivity.
1 code implementation • 28 Nov 2016 • Jianfeng Dong, Xiao-Jiao Mao, Chunhua Shen, Yu-Bin Yang
In this paper, we investigate convolutional denoising auto-encoders to show that unsupervised pre-training can still improve the performance of high-level image related tasks such as image classification and semantic segmentation.
17 code implementations • 29 Jun 2016 • Xiao-Jiao Mao, Chunhua Shen, Yu-Bin Yang
In this work, we propose a very deep fully convolutional auto-encoder network for image restoration, which is a encoding-decoding framework with symmetric convolutional-deconvolutional layers.
Ranked #2 on Grayscale Image Denoising on BSD200 sigma10
3 code implementations • NeurIPS 2016 • Xiao-Jiao Mao, Chunhua Shen, Yu-Bin Yang
We propose to symmetrically link convolutional and de-convolutional layers with skip-layer connections, with which the training converges much faster and attains a higher-quality local optimum.
Ranked #37 on Image Super-Resolution on BSD100 - 4x upscaling