3 research outputs found
VR-GNN: Variational Relation Vector Graph Neural Network for Modeling both Homophily and Heterophily
Graph Neural Networks (GNNs) have achieved remarkable success in diverse
real-world applications. Traditional GNNs are designed based on homophily,
which leads to poor performance under heterophily scenarios. Current solutions
deal with heterophily mainly by mixing high-order neighbors or passing signed
messages. However, mixing high-order neighbors destroys the original graph
structure and passing signed messages utilizes an inflexible message-passing
mechanism, which is prone to producing unsatisfactory effects. To overcome the
above problems, we propose a novel GNN model based on relation vector
translation named Variational Relation Vector Graph Neural Network (VR-GNN).
VR-GNN models relation generation and graph aggregation into an end-to-end
model based on Variational Auto-Encoder. The encoder utilizes the structure,
feature and label to generate a proper relation vector. The decoder achieves
superior node representation by incorporating the relation translation into the
message-passing framework. VR-GNN can fully capture the homophily and
heterophily between nodes due to the great flexibility of relation translation
in modeling neighbor relationships. We conduct extensive experiments on eight
real-world datasets with different homophily-heterophily properties to verify
the effectiveness of our model. The experimental results show that VR-GNN gains
consistent and significant improvements against state-of-the-art GNN methods
under heterophily, and competitive performance under homophily
Side-Scan Sonar Image Classification Based on Style Transfer and Pre-Trained Convolutional Neural Networks
Side-scan sonar is widely used in underwater rescue and the detection of undersea targets, such as shipwrecks, aircraft crashes, etc. Automatic object classification plays an important role in the rescue process to reduce the workload of staff and subjective errors caused by visual fatigue. However, the application of automatic object classification in side-scan sonar images is still lacking, which is due to a lack of datasets and the small number of image samples containing specific target objects. Secondly, the real data of side-scan sonar images are unbalanced. Therefore, a side-scan sonar image classification method based on synthetic data and transfer learning is proposed in this paper. In this method, optical images are used as inputs and the style transfer network is employed to simulate the side-scan sonar image to generate “simulated side-scan sonar images”; meanwhile, a convolutional neural network pre-trained on ImageNet is introduced for classification. In this paper, we experimentally demonstrate that the maximum accuracy of target classification is up to 97.32% by fine-tuning the pre-trained convolutional neural network using a training set incorporating “simulated side-scan sonar images”. The results show that the classification accuracy can be effectively improved by combining a pre-trained convolutional neural network and “similar side-scan sonar images”