activity
20202022
most citedAdaptive Multi-Teacher Multi-level Knowledge Distillation

222 citations · 256 across the 6 of their papers we have counts for

collaborators

6 papers

cs.SE2022

Multi-View Pre-Trained Model for Code Vulnerability Identification

Xuxiang Jiang, Yinhao Xiao, Jun Wang +1

Vulnerability identification is crucial for cyber security in the software-related industry. Early identification methods require significant manual efforts in crafting features or…

cs.LG2021★ 18 cited

Data-Free Knowledge Transfer: A Survey

Yuang Liu, Wei Zhang, Jun Wang +1

In the last decade, many deep learning models have been well trained and made a great success in various fields of machine intelligence, especially for computer vision and natural…

cs.CV2021★ 13 cited

Source-Free Domain Adaptation for Semantic Segmentation

Yuang Liu, Wei Zhang, Jun Wang

Unsupervised Domain Adaptation (UDA) can tackle the challenge that convolutional neural network(CNN)-based approaches for semantic segmentation heavily rely on the pixel-level anno…

cs.CV2021★ 1 cited

Zero-shot Adversarial Quantization

Yuang Liu, Wei Zhang, Jun Wang

Model quantization is a promising approach to compress deep neural networks and accelerate inference, making it possible to be deployed on mobile and edge devices. To retain the hi…

cs.CV2021★ 222 cited

Adaptive Multi-Teacher Multi-level Knowledge Distillation

Yuang Liu, Wei Zhang, Jun Wang

Knowledge distillation~(KD) is an effective learning paradigm for improving the performance of lightweight student networks by utilizing additional supervision knowledge distilled…

cs.CV2020★ 2 cited

Learning from a Lightweight Teacher for Efficient Knowledge Distillation

Yuang Liu, Wei Zhang, Jun Wang

Knowledge Distillation (KD) is an effective framework for compressing deep learning models, realized by a student-teacher paradigm requiring small student networks to mimic the sof…