10 citations · 11 across the 2 of their papers we have counts for
3 papers
cs.CV2020
Data-Free Knowledge Amalgamation via Group-Stack Dual-GAN
Jingwen Ye, Yixin Ji, Xinchao Wang +2
Recent advances in deep learning have provided procedures for learning one network to amalgamate multiple streams of knowledge from the pre-trained Convolutional Neural Network (CN…
cs.LG2019★ 1 cited
Amalgamating Filtered Knowledge: Learning Task-customized Student from Multi-task Teachers
Jingwen Ye, Xinchao Wang, Yixin Ji +2
Many well-trained Convolutional Neural Network(CNN) models have now been released online by developers for the sake of effortless reproducing. In this paper, we treat such pre-trai…
cs.CV2019★ 10 cited
Student Becoming the Master: Knowledge Amalgamation for Joint Scene Parsing, Depth Estimation, and More
Jingwen Ye, Yixin Ji, Xinchao Wang +3
In this paper, we investigate a novel deep-model reusing task. Our goal is to train a lightweight and versatile student model, without human-labelled annotations, that amalgamates…