5 papers
Geometric Mixture-of-Experts with Curvature-Guided Adaptive Routing for Graph Representation Learning
Haifang Cao, Yu Wang, Timing Li +2
Graph-structured data typically exhibits complex topological heterogeneity, making it difficult to model accurately within a single Riemannian manifold. While emerging mixed-curvat…
CKD: Contrastive Knowledge Distillation from A Sample-wise Perspective
Wencheng Zhu, Xin Zhou, Pengfei Zhu +2
In this paper, we propose a simple yet effective contrastive knowledge distillation framework that achieves sample-wise logit alignment while preserving semantic consistency. Conve…
BackMix: Regularizing Open Set Recognition by Removing Underlying Fore-Background Priors
Yu Wang, Junxian Mu, Hongzhi Huang +3
Open set recognition (OSR) requires models to classify known samples while detecting unknown samples for real-world applications. Existing studies show impressive progress using un…
Reducing Class-wise Confusion for Incremental Learning with Disentangled Manifolds
Huitong Chen, Yu Wang, Yan Fan +2
Class incremental learning (CIL) aims to enable models to continuously learn new classes without catastrophically forgetting old ones. A promising direction is to learn and use pro…
Dynamic Sub-graph Distillation for Robust Semi-supervised Continual Learning
Yan Fan, Yu Wang, Pengfei Zhu +1
Continual learning (CL) has shown promising results and comparable performance to learning at once in a fully supervised manner. However, CL strategies typically require a large nu…