83 citations · 398 across the 34 of their papers we have counts for
12 papers · 1 filter
DAC-MR: Data Augmentation Consistency Based Meta-Regularization for Meta-Learning
Jun Shu, Xiang Yuan, Deyu Meng +1
Meta learning recently has been heavily researched and helped advance the contemporary machine learning. However, achieving well-performing meta-learning model requires a large amo…
Regularize implicit neural representation by itself
Zhemin Li, Hongxia Wang, Deyu Meng
This paper proposes a regularizer called Implicit Neural Representation Regularizer (INRR) to improve the generalization ability of the Implicit Neural Representation (INR). The IN…
Diagnosing Batch Normalization in Class Incremental Learning
Minghao Zhou, Quanziang Wang, Jun Shu +2
Extensive researches have applied deep neural networks (DNNs) in class incremental learning (Class-IL). As building blocks of DNNs, batch normalization (BN) standardizes intermedia…
Investigating Bi-Level Optimization for Learning and Vision from a Unified Perspective: A Survey and Beyond
Risheng Liu, Jiaxin Gao, Jin Zhang +2
Bi-Level Optimization (BLO) is originated from the area of economic game theory and then introduced into the optimization community. BLO is able to handle problems with a hierarchi…
Select-ProtoNet: Learning to Select for Few-Shot Disease Subtype Prediction
Ziyi Yang, Jun Shu, Yong Liang +2
Current machine learning has made great progress on computer vision and many other fields attributed to the large amount of high-quality training samples, while it does not work ve…
Meta Transition Adaptation for Robust Deep Learning with Noisy Labels
Jun Shu, Qian Zhao, Zongben Xu +1
To discover intrinsic inter-class transition probabilities underlying data, learning with noise transition has become an important approach for robust deep learning on corrupted la…