Search Results for author:
Found 8 papers, 1 papers with code
Graph Partial Label Learning with Potential Cause Discovering
PLL is a critical weakly supervised learning problem, where each training instance is associated with a set of candidate labels, including both the true label and additional noisy labels.
Rethinking Causal Relationships Learning in Graph Neural Networks
In order to comprehensively analyze various GNN models from a causal learning perspective, we constructed an artificially synthesized dataset with known and controllable causal relationships between data and labels.
Unbiased Image Synthesis via Manifold Guidance in Diffusion Models
In response, we propose a plug-and-play method named Manifold Guidance Sampling, which is also the first unsupervised method to mitigate bias issue in DDPMs.
Manifold Constraint Regularization for Remote Sensing Image Generation
This study identifies a previously overlooked issue: GANs exhibit a heightened susceptibility to overfitting on remote sensing images. To address this challenge, this paper analyzes the characteristics of remote sensing images and proposes manifold constraint regularization, a novel approach that tackles overfitting of GANs on remote sensing images for the first time.
Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation
Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information.
Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective
By further observing the ramifications of introducing expertise logic into graph representation learning, we conclude that leading the GNNs to learn human expertise can improve the model performance.
A Research and Strategy of Remote Sensing Image Denoising Algorithms
Most raw data download from satellites are useless, resulting in transmission waste, one solution is to process data directly on satellites, then only transmit the processed results to the ground.
A Comparison and Strategy of Semantic Segmentation on Remote Sensing Images
But a large number of useless raw images, limited data storage resource and poor transmission capability on satellites hinder our use of valuable images.
