most citedAchieving adjustable elasticity with non-affine to affine transition

36 citations · 51 across the 5 of their papers we have counts for

collaborators

8 papers

cond-mat.mtrl-sci202136 cited

Achieving adjustable elasticity with non-affine to affine transition

Xiangying Shen, Chenchao Fang, Zhipeng Jin +7

For various engineering and industrial applications it is desirable to realize mechanical systems with broadly adjustable elasticity to respond flexibly to the external environment…

cs.CV20214 cited

Layerwise Optimization by Gradient Decomposition for Continual Learning

Shixiang Tang, Dapeng Chen, Jinguo Zhu +2

Deep neural networks achieve state-of-the-art and sometimes super-human performance across various domains. However, when learning tasks sequentially, the networks easily forget th…

cs.CV2021

Multiple Domain Experts Collaborative Learning: Multi-Source Domain Generalization For Person Re-Identification

Shijie Yu, Feng Zhu, Dapeng Chen +5

Recent years have witnessed significant progress in person re-identification (ReID). However, current ReID approaches still suffer from considerable performance degradation when un…

cs.CV20211 cited

Complementary Relation Contrastive Distillation

Jinguo Zhu, Shixiang Tang, Dapeng Chen +5

Knowledge distillation aims to transfer representation ability from a teacher model to a student model. Previous approaches focus on either individual representation distillation o…

cs.CV20219 cited

Gradient Regularized Contrastive Learning for Continual Domain Adaptation

Shixiang Tang, Peng Su, Dapeng Chen +1

Human beings can quickly adapt to environmental changes by leveraging learning experience. However, adapting deep neural networks to dynamic environments by machine learning algori…

cs.CV20201 cited

Gradient Regularized Contrastive Learning for Continual Domain Adaptation

Peng Su, Shixiang Tang, Peng Gao +3

Human beings can quickly adapt to environmental changes by leveraging learning experience. However, the poor ability of adapting to dynamic environments remains a major challenge f…