activity
20192022
most citedBe Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation

83 citations · 133 across the 12 of their papers we have counts for

collaborators

11 papers

cs.LG202118 cited

AFEC: Active Forgetting of Negative Transfer in Continual Learning

Liyuan Wang, Mingtian Zhang, Zhongfan Jia +5

Continual learning aims to learn a sequence of tasks from dynamic data distributions. Without accessing to the old training samples, knowledge transfer from the old tasks to each n…

math.OC20212 cited

Tightness and Equivalence of Semidefinite Relaxations for MIMO Detection

Ruichen Jiang, Ya-Feng Liu, Chenglong Bao +1

The multiple-input multiple-output (MIMO) detection problem, a fundamental problem in modern digital communications, is to detect a vector of transmitted symbols from the noisy out…

stat.ML20203 cited

Interpolation between Residual and Non-Residual Networks

Zonghan Yang, Yang Liu, Chenglong Bao +1

Although ordinary differential equations (ODEs) provide insights for designing network architectures, its relationship with the non-residual convolutional neural networks (CNNs) is…

math.NA2020

Efficient numerical methods for computing the stationary states of phase field crystal models

Kai Jiang, Wei Si, Chen Chang +1

Finding the stationary states of a free energy functional is an important problem in phase field crystal (PFC) models. Many efforts have been devoted for designing numerical scheme…

cs.CV20193 cited

Light-weight Calibrator: a Separable Component for Unsupervised Domain Adaptation

Shaokai Ye, Kailu Wu, Mu Zhou +6

Existing domain adaptation methods aim at learning features that can be generalized among domains. These methods commonly require to update source classifier to adapt to the target…

cs.CV20191 cited

Exploring Frequency Domain Interpretation of Convolutional Neural Networks

Zhongfan Jia, Chenglong Bao, Kaisheng Ma

Many existing interpretation methods of convolutional neural networks (CNNs) mainly analyze in spatial domain, yet model interpretability in frequency domain has been rarely studie…