16 citations · 24 across the 5 of their papers we have counts for
5 papers
Scalable Class-Incremental Learning Based on Parametric Neural Collapse
Chuangxin Zhang, Guangfeng Lin, Enhui Zhao +2
Incremental learning often encounter challenges such as overfitting to new data and catastrophic forgetting of old data. Existing methods can effectively extend the model for new t…
High-order structure preserving graph neural network for few-shot learning
Guangfeng Lin, Ying Yang, Yindi Fan +3
Few-shot learning can find the latent structure information between the prior knowledge and the queried data by the similarity metric of meta-learning to construct the discriminati…
Deep graph learning for semi-supervised classification
Guangfeng Lin, Xiaobing Kang, Kaiyang Liao +2
Graph learning (GL) can dynamically capture the distribution structure (graph structure) of data based on graph convolutional networks (GCN), and the learning quality of the graph…
Structure fusion based on graph convolutional networks for semi-supervised classification
Guangfeng Lin, Jing Wang, Kaiyang Liao +2
Suffering from the multi-view data diversity and complexity for semi-supervised classification, most of existing graph convolutional networks focus on the networks architecture con…
Transfer feature generating networks with semantic classes structure for zero-shot learning
Guangfeng Lin, Wanjun Chen, Kaiyang Liao +2
Feature generating networks face to the most important question, which is the fitting difference (inconsistence) of the distribution between the generated feature and the real data…