Search Results for author:
Found 7 papers, 4 papers with code
T2-GNN: Graph Neural Networks for Graphs with Incomplete Features and Structure via Teacher-Student Distillation
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.
From Easy to Hard: Two-stage Selector and Reader for Multi-hop Question Answering
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.
ResT V2: Simpler, Faster and Stronger
This paper proposes ResTv2, a simpler, faster, and stronger multi-scale vision Transformer for visual recognition.
Optimisation of Air-Ground Swarm Teaming for Target Search, using Differential Evolution
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.
Learning Deep Representations Using Convolutional Auto-encoders with Symmetric Skip Connections
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.
Image Restoration Using Convolutional Auto-encoders with Symmetric Skip Connections
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
Image Restoration Using Very Deep Convolutional Encoder-Decoder Networks with Symmetric Skip Connections
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
