Search Results for author: Bin Zhong

Found 3 papers, 1 papers with code

CLS: Cross Labeling Supervision for Semi-Supervised Learning

no code implementations17 Feb 2022 Yao Yao, Junyi Shen, Jin Xu, Bin Zhong, Li Xiao

Based on FixMatch, where a pseudo label is generated from a weakly-augmented sample to teach the prediction on a strong augmentation of the same input sample, CLS allows the creation of both pseudo and complementary labels to support both positive and negative learning.

pseudo label

Weak Supervision for Fake News Detection via Reinforcement Learning

1 code implementation28 Dec 2019 Yaqing Wang, Weifeng Yang, Fenglong Ma, Jin Xu, Bin Zhong, Qiang Deng, Jing Gao

In order to tackle this challenge, we propose a reinforced weakly-supervised fake news detection framework, i. e., WeFEND, which can leverage users' reports as weak supervision to enlarge the amount of training data for fake news detection.

Fake News Detection reinforcement-learning

Transfer Value Iteration Networks

no code implementations11 Nov 2019 Junyi Shen, Hankz Hankui Zhuo, Jin Xu, Bin Zhong, Sinno Jialin Pan

However, based on our experiments, a policy learned by VINs still fail to generalize well on the domain whose action space and feature space are not identical to those in the domain where it is trained.

Transfer Learning

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