1 code implementation • 3 Jul 2023 • Chaoxi Niu, Guansong Pang, Ling Chen
One primary challenge is to learn normal patterns manifested in both fine-grained and holistic views of graphs for identifying graphs that are abnormal in part or in whole.
1 code implementation • 31 Jan 2023 • Chaoxi Niu, Guansong Pang, Ling Chen
To tackle this problem, this article proposes a novel approach that builds a discriminative model on collective affinity information (i. e., two sets of pairwise affinities between the negative instances and the anchor instance) to mine hard negatives in GCL.
1 code implementation • 2 Sep 2020 • Kun Zhan, Chaoxi Niu
We propose a new training method named as mutual teaching, i. e., we train dual models and let them teach each other during each batch.
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1 code implementation • 19 Jun 2019 • Changlu Chen, Chaoxi Niu, Xia Zhan, Kun Zhan
Based on the pretrained model and the constructed graph, we add a self-expressive layer to complete the generative model and then fine-tune it with a new loss function, including the reconstruction loss and a deliberately defined locality-preserving loss.