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Found 10 papers, 5 papers with code
Unleashing the potential of GNNs via Bi-directional Knowledge Transfer
On this basis, BiKT not only allows us to acquire knowledge from both the GNN and its derived model but promotes each other by injecting the knowledge into the other.
Node-oriented Spectral Filtering for Graph Neural Networks
Graph neural networks (GNNs) have shown remarkable performance on homophilic graph data while being far less impressive when handling non-homophilic graph data due to the inherent low-pass filtering property of GNNs.
Multi-modal Graph Learning for Disease Prediction
For disease prediction tasks, most existing graph-based methods tend to define the graph manually based on specified modality (e. g., demographic information), and then integrated other modalities to obtain the patient representation by Graph Representation Learning (GRL).
CETransformer: Casual Effect Estimation via Transformer Based Representation Learning
Treatment effect estimation, which refers to the estimation of causal effects and aims to measure the strength of the causal relationship, is of great importance in many fields but is a challenging problem in practice.
Multi-modal Graph Learning for Disease Prediction
However, it is not easy for these approaches to generalize to unseen samples.
Margin Preserving Self-paced Contrastive Learning Towards Domain Adaptation for Medical Image Segmentation
To bridge the gap between the source and target domains in unsupervised domain adaptation (UDA), the most common strategy puts focus on matching the marginal distributions in the feature space through adversarial learning.
Adversarial Graph Disentanglement
For them, a component-specific aggregation approach is proposed to achieve micro-disentanglement by inferring latent components that cause the links between nodes.
Taking Modality-free Human Identification as Zero-shot Learning
There have been numerous methods proposed for human identification, such as face identification, person re-identification, and gait identification.
Distribution-induced Bidirectional Generative Adversarial Network for Graph Representation Learning
Graph representation learning aims to encode all nodes of a graph into low-dimensional vectors that will serve as input of many compute vision tasks.
Generative Adversarial Network
Graph Representation Learning
Convolutional Prototype Learning for Zero-Shot Recognition
The key to ZSL is to transfer knowledge from the seen to the unseen classes via auxiliary class attribute vectors.
