5 citations · 10 across the 4 of their papers we have counts for
5 papers
SurroundNet: Towards Effective Low-Light Image Enhancement
Fei Zhou, Xin Sun, Junyu Dong +2
Although Convolution Neural Networks (CNNs) has made substantial progress in the low-light image enhancement task, one critical problem of CNNs is the paradox of model complexity a…
Knowledge Distillation via Instance-level Sequence Learning
Haoran Zhao, Xin Sun, Junyu Dong +2
Recently, distillation approaches are suggested to extract general knowledge from a teacher network to guide a student network. Most of the existing methods transfer knowledge from…
Dual Discriminator Adversarial Distillation for Data-free Model Compression
Haoran Zhao, Xin Sun, Junyu Dong +2
Knowledge distillation has been widely used to produce portable and efficient neural networks which can be well applied on edge devices for computer vision tasks. However, almost a…
Similarity Transfer for Knowledge Distillation
Haoran Zhao, Kun Gong, Xin Sun +2
Knowledge distillation is a popular paradigm for learning portable neural networks by transferring the knowledge from a large model into a smaller one. Most existing approaches enh…
Highlight Every Step: Knowledge Distillation via Collaborative Teaching
Haoran Zhao, Xin Sun, Junyu Dong +2
High storage and computational costs obstruct deep neural networks to be deployed on resource-constrained devices. Knowledge distillation aims to train a compact student network by…