4 papers
Towards Task Sampler Learning for Meta-Learning
Jingyao Wang, Wenwen Qiang, Xingzhe Su +3
Meta-learning aims to learn general knowledge with diverse training tasks conducted from limited data, and then transfer it to new tasks. It is commonly believed that increasing ta…
Hacking Task Confounder in Meta-Learning
Jingyao Wang, Yi Ren, Zeen Song +3
Meta-learning enables rapid generalization to new tasks by learning knowledge from various tasks. It is intuitively assumed that as the training progresses, a model will acquire ri…
Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation
Xingzhe Su, Wenwen Qiang, Jie Hu +3
Generative adversarial networks (GANs) have achieved remarkable progress in the natural image field. However, when applying GANs in the remote sensing (RS) image generation task, a…
Unbiased Image Synthesis via Manifold Guidance in Diffusion Models
Xingzhe Su, Daixi Jia, Fengge Wu +3
Diffusion Models are a potent class of generative models capable of producing high-quality images. However, they often inadvertently favor certain data attributes, undermining the…