398 citations · 466 across the 9 of their papers we have counts for
9 papers
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…
Improve Noise Tolerance of Robust Loss via Noise-Awareness
Kehui Ding, Jun Shu, Deyu Meng +1
Robust loss minimization is an important strategy for handling robust learning issue on noisy labels. Current approaches for designing robust losses involve the introduction of noi…
A Data-driven Loss Weighting Scheme across Heterogeneous Tasks for Image Denoising
Xiangyu Rui, Xiangyong Cao, Xile Zhao +2
In a variational denoising model, weight in the data fidelity term plays the role of enhancing the noise-removal capability. It is profoundly correlated with noise information, whi…
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…
CMW-Net: Learning a Class-Aware Sample Weighting Mapping for Robust Deep Learning
Jun Shu, Xiang Yuan, Deyu Meng +1
Modern deep neural networks can easily overfit to biased training data containing corrupted labels or class imbalance. Sample re-weighting methods are popularly used to alleviate t…
Learning an Explicit Hyperparameter Prediction Function Conditioned on Tasks
Jun Shu, Deyu Meng, Zongben Xu
Meta learning has attracted much attention recently in machine learning community. Contrary to conventional machine learning aiming to learn inherent prediction rules to predict la…
MLR-SNet: Transferable LR Schedules for Heterogeneous Tasks
Jun Shu, Yanwen Zhu, Qian Zhao +2
The learning rate (LR) is one of the most important hyper-parameters in stochastic gradient descent (SGD) algorithm for training deep neural networks (DNN). However, current hand-d…
Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting
Jun Shu, Qi Xie, Lixuan Yi +4
Current deep neural networks (DNNs) can easily overfit to biased training data with corrupted labels or class imbalance. Sample re-weighting strategy is commonly used to alleviate…
Small Sample Learning in Big Data Era
Jun Shu, Zongben Xu, Deyu Meng
As a promising area in artificial intelligence, a new learning paradigm, called Small Sample Learning (SSL), has been attracting prominent research attention in the recent years. I…