11 citations · 14 across the 6 of their papers we have counts for
7 papers
Generating Linear programming Instances with Controllable Rank and Condition Number
Anqi Li, Congying Han, Tiande Guo
Instances generation is crucial for linear programming algorithms, which is necessary either to find the optimal pivot rules by training learning method or to evaluate and verify c…
Masked Reconstruction Contrastive Learning with Information Bottleneck Principle
Ziwen Liu, Bonan Li, Congying Han +2
Contrastive learning (CL) has shown great power in self-supervised learning due to its ability to capture insight correlations among large-scale data. Current CL models are biased…
Generalized One-shot Domain Adaptation of Generative Adversarial Networks
Zicheng Zhang, Yinglu Liu, Congying Han +3
The adaptation of a Generative Adversarial Network (GAN) aims to transfer a pre-trained GAN to a target domain with limited training data. In this paper, we focus on the one-shot c…
PetsGAN: Rethinking Priors for Single Image Generation
Zicheng Zhang, Yinglu Liu, Congying Han +3
Single image generation (SIG), described as generating diverse samples that have similar visual content with the given single image, is first introduced by SinGAN which builds a py…
DFS: A Diverse Feature Synthesis Model for Generalized Zero-Shot Learning
Bonan Li, Xuecheng Nie, Congying Han
Generative based strategy has shown great potential in the Generalized Zero-Shot Learning task. However, it suffers severe generalization problem due to lacking of feature diversit…
Learning Graph Representation by Aggregating Subgraphs via Mutual Information Maximization
Chenguang Wang, Ziwen Liu
In this paper, we introduce a self-supervised learning method to enhance the graph-level representations with the help of a set of subgraphs. For this purpose, we propose a univers…