Search Results for author:
Found 3 papers, 1 papers with code
Active Generation Network of Human Skeleton for Action Recognition
To solve those problems, We propose a novel active generative network (AGN), which can adaptively learn various action categories by motion style transfer to generate new actions when the data for a particular action is only a single sample or few samples.
Dynamic Domain Discrepancy Adjustment for Active Multi-Domain Adaptation
This mechanism controls the alignment level of features between each source domain and the target domain, effectively leveraging the local advantageous feature information within the source domains.
Multi-Source Unsupervised Domain Adaptation
Unsupervised Domain Adaptation
Improving Knowledge Distillation via Transferring Learning Ability
Existing knowledge distillation methods generally use a teacher-student approach, where the student network solely learns from a well-trained teacher.
