92 citations · 318 across the 29 of their papers we have counts for
6 papers · 1 filter
WeMix: How to Better Utilize Data Augmentation
Yi Xu, Asaf Noy, Ming Lin +3
Data augmentation is a widely used training trick in deep learning to improve the network generalization ability. Despite many encouraging results, several recent studies did point…
Semi-Anchored Detector for One-Stage Object Detection
Lei Chen, Qi Qian, Hao Li
A standard one-stage detector is comprised of two tasks: classification and regression. Anchors of different shapes are introduced for each location in the feature map to mitigate…
Improved Knowledge Distillation via Full Kernel Matrix Transfer
Qi Qian, Hao Li, Juhua Hu
Knowledge distillation is an effective way for model compression in deep learning. Given a large model (i.e., teacher model), it aims to improve the performance of a compact model…
Neural Architecture Design for GPU-Efficient Networks
Ming Lin, Hesen Chen, Xiuyu Sun +3
Many mission-critical systems are based on GPU for inference. It requires not only high recognition accuracy but also low latency in responding time. Although many studies are devo…
Towards Understanding Label Smoothing
Yi Xu, Yuanhong Xu, Qi Qian +2
Label smoothing regularization (LSR) has a great success in training deep neural networks by stochastic algorithms such as stochastic gradient descent and its variants. However, th…
Weakly Supervised Representation Learning with Coarse Labels
Yuanhong Xu, Qi Qian, Hao Li +2
With the development of computational power and techniques for data collection, deep learning demonstrates a superior performance over most existing algorithms on visual benchmark…