activity
20182024
most citedBe Your Own Teacher: Improve the Performance of Convolutional Neural Networks via Self Distillation

83 citations · 145 across the 14 of their papers we have counts for

collaborators

18 papers

cs.CV20222 cited

Structured Knowledge Distillation Towards Efficient and Compact Multi-View 3D Detection

Linfeng Zhang, Yukang Shi, Hung-Shuo Tai +4

Detecting 3D objects from multi-view images is a fundamental problem in 3D computer vision. Recently, significant breakthrough has been made in multi-view 3D detection tasks. Howev…

cs.CV20223 cited

LW-ISP: A Lightweight Model with ISP and Deep Learning

Hongyang Chen, Kaisheng Ma

The deep learning (DL)-based methods of low-level tasks have many advantages over the traditional camera in terms of hardware prospects, error accumulation and imaging effects. Rec…

cs.CV20222 cited

Region-aware Knowledge Distillation for Efficient Image-to-Image Translation

Linfeng Zhang, Xin Chen, Runpei Dong +1

Recent progress in image-to-image translation has witnessed the success of generative adversarial networks (GANs). However, GANs usually contain a huge number of parameters, which…

cs.CV20222 cited

PointDistiller: Structured Knowledge Distillation Towards Efficient and Compact 3D Detection

Linfeng Zhang, Runpei Dong, Hung-Shuo Tai +1

The remarkable breakthroughs in point cloud representation learning have boosted their usage in real-world applications such as self-driving cars and virtual reality. However, thes…

cs.CV2022

Unsupervised Deep Learning Meets Chan-Vese Model

Dihan Zheng, Chenglong Bao, Zuoqiang Shi +2

The Chan-Vese (CV) model is a classic region-based method in image segmentation. However, its piecewise constant assumption does not always hold for practical applications. Many im…

cs.CV2022

Wavelet Knowledge Distillation: Towards Efficient Image-to-Image Translation

Linfeng Zhang, Xin Chen, Xiaobing Tu +3

Remarkable achievements have been attained with Generative Adversarial Networks (GANs) in image-to-image translation. However, due to a tremendous amount of parameters, state-of-th…