Search Results for author: Xingzhe Su

Found 7 papers, 1 papers with code

Towards Task Sampler Learning for Meta-Learning

1 code implementation18 Jul 2023 Jingyao Wang, Wenwen Qiang, Xingzhe Su, Changwen Zheng, Fuchun Sun, Hui Xiong

We obtain three conclusions: (i) there is no universal task sampling strategy that can guarantee the optimal performance of meta-learning models; (ii) over-constraining task diversity may incur the risk of under-fitting or over-fitting during training; and (iii) the generalization performance of meta-learning models are affected by task diversity, task entropy, and task difficulty.

Few-Shot Learning General Knowledge

Towards the Sparseness of Projection Head in Self-Supervised Learning

no code implementations18 Jul 2023 Zeen Song, Xingzhe Su, Jingyao Wang, Wenwen Qiang, Changwen Zheng, Fuchun Sun

In recent years, self-supervised learning (SSL) has emerged as a promising approach for extracting valuable representations from unlabeled data.

Contrastive Learning Self-Supervised Learning

Unbiased Image Synthesis via Manifold-Driven Sampling in Diffusion Models

no code implementations17 Jul 2023 Xingzhe Su, Yi Ren, Wenwen Qiang, Zeen Song, Hang Gao, Fengge Wu, Changwen Zheng

Diffusion models are a potent class of generative models capable of producing high-quality images.

Image Generation

Manifold Constraint Regularization for Remote Sensing Image Generation

no code implementations31 May 2023 Xingzhe Su, Changwen Zheng, Wenwen Qiang, Fengge Wu, Junsuo Zhao, Fuchun Sun, Hui Xiong

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.

Image Generation

Intriguing Property and Counterfactual Explanation of GAN for Remote Sensing Image Generation

no code implementations9 Mar 2023 Xingzhe Su, Wenwen Qiang, Jie Hu, Fengge Wu, Changwen Zheng, Fuchun Sun

Based on this SCM, we theoretically prove that the quality of generated images is positively correlated with the amount of feature information.

counterfactual Counterfactual Explanation +1

Introducing Expertise Logic into Graph Representation Learning from A Causal Perspective

no code implementations20 Jan 2023 Hang Gao, Jiangmeng Li, Wenwen Qiang, Lingyu Si, Xingzhe Su, Fengge Wu, Changwen Zheng, Fuchun Sun

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.

Graph Representation Learning Knowledge Graphs

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