36 citations · 37 across the 9 of their papers we have counts for
10 papers
Channel Self-Supervision for Online Knowledge Distillation
Shixiao Fan, Xuan Cheng, Xiaomin Wang +5
Recently, researchers have shown an increased interest in the online knowledge distillation. Adopting an one-stage and end-to-end training fashion, online knowledge distillation us…
Temporally Resolution Decrement: Utilizing the Shape Consistency for Higher Computational Efficiency
Tianshu Xie, Xuan Cheng, Minghui Liu +3
Image resolution that has close relations with accuracy and computational cost plays a pivotal role in network training. In this paper, we observe that the reduced image retains re…
Feature Mining: A Novel Training Strategy for Convolutional Neural Network
Tianshu Xie, Xuan Cheng, Xiaomin Wang +3
In this paper, we propose a novel training strategy for convolutional neural network(CNN) named Feature Mining, that aims to strengthen the network's learning of the local feature.…
Go Small and Similar: A Simple Output Decay Brings Better Performance
Xuan Cheng, Tianshu Xie, Xiaomin Wang +3
Regularization and data augmentation methods have been widely used and become increasingly indispensable in deep learning training. Researchers who devote themselves to this have c…
Self-supervision of Feature Transformation for Further Improving Supervised Learning
Zilin Ding, Yuhang Yang, Xuan Cheng +2
Self-supervised learning, which benefits from automatically constructing labels through pre-designed pretext task, has recently been applied for strengthen supervised learning. Sin…
Self-supervised Feature Enhancement: Applying Internal Pretext Task to Supervised Learning
Yuhang Yang, Zilin Ding, Xuan Cheng +2
Traditional self-supervised learning requires CNNs using external pretext tasks (i.e., image- or video-based tasks) to encode high-level semantic visual representations. In this pa…
White Paper Assistance: A Step Forward Beyond the Shortcut Learning
Xuan Cheng, Tianshu Xie, Xiaomin Wang +3
The promising performances of CNNs often overshadow the need to examine whether they are doing in the way we are actually interested. We show through experiments that even over-par…
FocusedDropout for Convolutional Neural Network
Tianshu Xie, Minghui Liu, Jiali Deng +3
In convolutional neural network (CNN), dropout cannot work well because dropped information is not entirely obscured in convolutional layers where features are correlated spatially…
Selective Output Smoothing Regularization: Regularize Neural Networks by Softening Output Distributions
Xuan Cheng, Tianshu Xie, Xiaomin Wang +4
In this paper, we propose Selective Output Smoothing Regularization, a novel regularization method for training the Convolutional Neural Networks (CNNs). Inspired by the diverse ef…
Cut-Thumbnail: A Novel Data Augmentation for Convolutional Neural Network
Tianshu Xie, Xuan Cheng, Minghui Liu +3
In this paper, we propose a novel data augmentation strategy named Cut-Thumbnail, that aims to improve the shape bias of the network. We reduce an image to a certain size and repla…