AmGCL: Feature Imputation of Attribute Missing Graph via Self-supervised Contrastive Learning

no code implementations • 5 May 2023 • Xiaochuan Zhang, Mengran Li, Ye Wang, Haojun Fei

To address these challenges, we propose Attribute missing Graph Contrastive Learning (AmGCL), a framework for handling missing node attributes in attribute graph data.

Attribute Contrastive Learning +3

AACC: Asymmetric Actor-Critic in Contextual Reinforcement Learning

no code implementations • 3 Aug 2022 • Wangyang Yue, Yuan Zhou, Xiaochuan Zhang, Yuchen Hua, Zhiyuan Wang, Guang Kou

Various methods, such as domain randomization, have been proposed to deal with such situations by training agents under different environmental setups, and therefore they can be generalized to different environments during deployment.

reinforcement-learning Reinforcement Learning (RL)

Achieving Domain Generalization in Underwater Object Detection by Domain Mixup and Contrastive Learning

no code implementations • 6 Apr 2021 • Yang Chen, Pinhao Song, Hong Liu, Linhui Dai, Xiaochuan Zhang, Runwei Ding, Shengquan Li

Second, for the images with the same semantic content in different domains, their hidden features should be equivalent.

Contrastive Learning Data Augmentation +5