16 citations · 54 across the 10 of their papers we have counts for
10 papers
Generalized Knowledge Distillation via Relationship Matching
Han-Jia Ye, Su Lu, De-Chuan Zhan
The knowledge of a well-trained deep neural network (a.k.a. the "teacher") is valuable for learning similar tasks. Knowledge distillation extracts knowledge from the teacher and in…
Identifying Ambiguous Similarity Conditions via Semantic Matching
Han-Jia Ye, Yi Shi, De-Chuan Zhan
Rich semantics inside an image result in its ambiguous relationship with others, i.e., two images could be similar in one condition but dissimilar in another. Given triplets like "…
Forward Compatible Few-Shot Class-Incremental Learning
Da-Wei Zhou, Fu-Yun Wang, Han-Jia Ye +3
Novel classes frequently arise in our dynamically changing world, e.g., new users in the authentication system, and a machine learning model should recognize new classes without fo…
Co-Transport for Class-Incremental Learning
Da-Wei Zhou, Han-Jia Ye, De-Chuan Zhan
Traditional learning systems are trained in closed-world for a fixed number of classes, and need pre-collected datasets in advance. However, new classes often emerge in real-world…
Few-Shot Action Recognition with Compromised Metric via Optimal Transport
Su Lu, Han-Jia Ye, De-Chuan Zhan
Although vital to computer vision systems, few-shot action recognition is still not mature despite the wide research of few-shot image classification. Popular few-shot learning alg…
Procrustean Training for Imbalanced Deep Learning
Han-Jia Ye, De-Chuan Zhan, Wei-Lun Chao
Neural networks trained with class-imbalanced data are known to perform poorly on minor classes of scarce training data. Several recent works attribute this to over-fitting to mino…