no code implementations • 28 Jan 2024 • Kangkang Lu, Yanhua Yu, Hao Fei, Xuan Li, Zixuan Yang, Zirui Guo, Meiyu Liang, Mengran Yin, Tat-Seng Chua
Moreover, we theoretically establish that the number of distinguishable eigenvalues plays a pivotal role in determining the expressive power of spectral graph neural networks.
2 code implementations • ACM Multimedia 2022 • Meiyu Liang, Junping Du, Xiaowen Cao, Yang Yu, Kangkang Lu, Zhe Xue, Min Zhang
Secondly, for further improving learning ability of implicit cross-media semantic associations, a semantic label association graph is constructed, and the graph convolutional network is utilized to mine the implicit semantic structures, thus guiding learning of discriminative features of different modalities.
1 code implementation • 18 May 2022 • Xun Xu, Manh Cuong Nguyen, Yasin Yazici, Kangkang Lu, Hlaing Min, Chuan-Sheng Foo
In this work, we propose SemiCurv, a semi-supervised learning (SSL) framework for curvilinear structure segmentation that is able to utilize such unlabelled data to reduce the labelling burden.
no code implementations • 6 May 2022 • Xun Xu, Jingyi Liao, Lile Cai, Manh Cuong Nguyen, Kangkang Lu, Wanyue Zhang, Yasin Yazici, Chuan Sheng Foo
Recent studies combined finetuning (FT) from pretrained weights with SSL to mitigate the challenges and claimed superior results in the low-label regime.
no code implementations • 29 Sep 2021 • Cuong Manh Nguyen, Le Zhang, Arun Raja, Xun Xu, Balagopal Unnikrishnan, Kangkang Lu, Chuan-Sheng Foo
Label collection is costly in many applications, which poses the need for label-efficient learning.
1 code implementation • 13 Jan 2021 • Govind Narasimman, Kangkang Lu, Arun Raja, Chuan Sheng Foo, Mohamed Sabry Aly, Jie Lin, Vijay Chandrasekhar
Despite the vast literature on Human Activity Recognition (HAR) with wearable inertial sensor data, it is perhaps surprising that there are few studies investigating semisupervised learning for HAR, particularly in a challenging scenario with class imbalance problem.
no code implementations • ICLR 2021 • Kangkang Lu, Cuong Manh Nguyen, Xun Xu, Kiran Chari, Yu Jing Goh, Chuan-Sheng Foo
In this paper, we propose ARMOURED, an adversarially robust training method based on semi-supervised learning that consists of two components.